2022-04-15 11:38:24 +08:00
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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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2024-07-10 13:51:34 +08:00
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from __future__ import annotations
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2024-08-10 12:05:32 +08:00
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from typing import TYPE_CHECKING, Literal
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2024-07-10 13:51:34 +08:00
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2022-04-15 11:38:24 +08:00
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__all__ = []
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2024-08-16 14:21:19 +08:00
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from paddle import _C_ops
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2023-09-07 17:26:19 +08:00
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from paddle.base.layer_helper import LayerHelper
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2024-06-12 10:56:56 +08:00
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from paddle.framework import in_dynamic_or_pir_mode
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2022-04-15 11:38:24 +08:00
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from paddle.nn.functional.conv import _update_padding_nd
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2023-03-09 20:30:52 +08:00
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from paddle.utils import convert_to_list
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2022-11-29 18:50:04 +08:00
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from ...binary import add
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2022-04-15 11:38:24 +08:00
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2024-07-10 13:51:34 +08:00
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if TYPE_CHECKING:
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2024-08-10 12:05:32 +08:00
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from collections.abc import Sequence
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2024-07-10 13:51:34 +08:00
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from paddle import Tensor
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from paddle._typing import (
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DataLayout2D,
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DataLayout3D,
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Size2,
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Size3,
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Size4,
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Size6,
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)
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from paddle.nn.functional.common import _PaddingSizeMode
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2022-04-15 11:38:24 +08:00
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2022-10-23 20:01:27 +08:00
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def _conv3d(
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x: Tensor,
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weight: Tensor,
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bias: Tensor | None = None,
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stride: Size3 = 1,
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padding: _PaddingSizeMode | Size3 | Size6 | Sequence[Size2] = 0,
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dilation: Size3 = 1,
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groups: Literal[1] = 1,
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subm: bool = False,
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key: str | None = None,
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data_format: Literal["NDHWC"] = "NDHWC",
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name: str | None = None,
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) -> Tensor:
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assert groups == 1, "Currently, only support groups=1"
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dims = 3
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# Currently, only support 'NDHWC'
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if data_format not in ["NDHWC"]:
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2022-10-23 20:01:27 +08:00
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raise ValueError(
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"Attr(data_format) should be 'NDHWC'. Received "
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2023-09-22 10:14:38 +08:00
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f"Attr(data_format): {data_format}."
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2022-10-23 20:01:27 +08:00
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)
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2022-04-15 11:38:24 +08:00
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if len(x.shape) != 5:
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raise ValueError(
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2023-09-22 10:14:38 +08:00
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f"Input x should be 5D tensor, but received x with the shape of {x.shape}"
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2022-10-23 20:01:27 +08:00
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)
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channel_last = data_format == "NDHWC"
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channel_dim = -1 if channel_last else 1
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num_channels = x.shape[channel_dim]
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padding, padding_algorithm = _update_padding_nd(padding, channel_last, dims)
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stride = convert_to_list(stride, dims, 'stride')
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dilation = convert_to_list(dilation, dims, 'dilation')
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2024-06-12 10:56:56 +08:00
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if in_dynamic_or_pir_mode():
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2022-10-23 20:01:27 +08:00
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pre_bias = _C_ops.sparse_conv3d(
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x,
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weight,
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padding,
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dilation,
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stride,
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groups,
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subm,
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key if key is not None else "",
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)
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2022-09-27 11:20:41 +08:00
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if bias is not None:
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return add(pre_bias, bias)
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else:
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return pre_bias
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2022-04-22 16:46:47 +08:00
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else:
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2022-09-27 11:20:41 +08:00
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inputs = {'x': x, 'kernel': weight}
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attrs = {
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'paddings': padding,
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'dilations': dilation,
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'strides': stride,
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'groups': groups,
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'subm': subm,
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'key': key,
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}
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op_type = 'sparse_conv3d'
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helper = LayerHelper(op_type, **locals())
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2022-10-23 20:01:27 +08:00
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rulebook = helper.create_variable_for_type_inference(
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dtype='int32', stop_gradient=True
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)
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counter = helper.create_variable_for_type_inference(
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dtype='int32', stop_gradient=True
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)
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pre_bias = helper.create_sparse_variable_for_type_inference(x.dtype)
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outputs = {"out": pre_bias, "rulebook": rulebook, "counter": counter}
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2022-10-23 20:01:27 +08:00
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helper.append_op(
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type=op_type, inputs=inputs, outputs=outputs, attrs=attrs
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)
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2022-09-27 11:20:41 +08:00
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if bias is not None:
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return add(pre_bias, bias)
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else:
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return pre_bias
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2022-04-15 11:38:24 +08:00
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2023-06-01 21:58:31 +08:00
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def _conv2d(
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x: Tensor,
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weight: Tensor,
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bias: Tensor | None = None,
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stride: Size2 = 1,
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padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0,
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dilation: Size2 = 1,
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groups: Literal[1] = 1,
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subm: bool = False,
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key: str | None = None,
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data_format: Literal["NHWC"] = "NHWC",
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name: str | None = None,
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) -> Tensor:
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2023-06-01 21:58:31 +08:00
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assert groups == 1, "Currently, only support groups=1"
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dims = 2
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# Currently, only support 'NHWC'
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if data_format not in ["NHWC"]:
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raise ValueError(
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"Attr(data_format) should be 'NHWC'. Received "
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2023-09-22 10:14:38 +08:00
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f"Attr(data_format): {data_format}."
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2023-06-01 21:58:31 +08:00
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)
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if len(x.shape) != 4:
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raise ValueError(
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f"Input x should be 4D tensor, but received x with the shape of {x.shape}"
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2023-06-01 21:58:31 +08:00
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)
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channel_last = data_format == "NHWC"
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channel_dim = -1 if channel_last else 1
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if len(x.shape) != 4:
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raise ValueError(
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2023-09-22 10:14:38 +08:00
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f"Input x should be 4D tensor, but received x with the shape of {x.shape}"
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2023-06-01 21:58:31 +08:00
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)
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num_channels = x.shape[channel_dim]
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padding, padding_algorithm = _update_padding_nd(padding, channel_last, dims)
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stride = convert_to_list(stride, dims, 'stride')
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dilation = convert_to_list(dilation, dims, 'dilation')
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2024-06-12 10:56:56 +08:00
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if in_dynamic_or_pir_mode():
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2023-06-01 21:58:31 +08:00
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pre_bias = _C_ops.sparse_conv3d(
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x,
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weight,
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padding,
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dilation,
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stride,
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groups,
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subm,
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key if key is not None else "",
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)
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else:
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2023-06-13 14:48:06 +08:00
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inputs = {'x': x, 'kernel': weight}
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attrs = {
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'paddings': padding,
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'dilations': dilation,
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'strides': stride,
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'groups': groups,
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'subm': subm,
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'key': key,
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}
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op_type = 'sparse_conv3d'
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helper = LayerHelper(op_type, **locals())
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rulebook = helper.create_variable_for_type_inference(
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dtype='int32', stop_gradient=True
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)
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counter = helper.create_variable_for_type_inference(
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dtype='int32', stop_gradient=True
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)
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pre_bias = helper.create_sparse_variable_for_type_inference(x.dtype)
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outputs = {"out": pre_bias, "rulebook": rulebook, "counter": counter}
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helper.append_op(
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type=op_type, inputs=inputs, outputs=outputs, attrs=attrs
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)
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if bias is not None:
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return add(pre_bias, bias)
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else:
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return pre_bias
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2023-06-01 21:58:31 +08:00
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2024-04-22 13:33:02 +08:00
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def _conv3d_igemm(
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x: Tensor,
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weight: Tensor,
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bias: Tensor | None = None,
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stride: Size3 = 1,
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padding: _PaddingSizeMode | Size3 | Size6 | Sequence[Size2] = 0,
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dilation: Size3 = 1,
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groups: Literal[1] = 1,
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subm: Literal[True] = True,
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key: str | None = None,
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data_format: Literal["NDHWC"] = "NDHWC",
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name: str | None = None,
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) -> Tensor:
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2024-04-22 13:33:02 +08:00
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assert groups == 1, "Currently, only support groups=1"
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assert subm is True, "Currently, only support subm=True for implicit gemm"
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dims = 3
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# Currently, only support 'NDHWC'
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if data_format not in ["NDHWC"]:
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raise ValueError(
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"Attr(data_format) should be 'NDHWC'. Received "
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f"Attr(data_format): {data_format}."
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)
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if len(x.shape) != 5:
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raise ValueError(
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f"Input x should be 5D tensor, but received x with the shape of {x.shape}"
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)
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channel_last = data_format == "NDHWC"
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channel_dim = -1 if channel_last else 1
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if len(x.shape) != 5:
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raise ValueError(
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f"Input x should be 5D tensor, but received x with the shape of {x.shape}"
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)
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num_channels = x.shape[channel_dim]
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padding, padding_algorithm = _update_padding_nd(padding, channel_last, dims)
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stride = convert_to_list(stride, dims, 'stride')
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dilation = convert_to_list(dilation, dims, 'dilation')
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2024-06-12 10:56:56 +08:00
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if in_dynamic_or_pir_mode():
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2024-04-22 13:33:02 +08:00
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pre_bias = _C_ops.sparse_conv3d_implicit_gemm(
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x,
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weight,
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padding,
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dilation,
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stride,
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groups,
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subm,
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key if key is not None else "",
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)
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if bias is not None:
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return add(pre_bias, bias)
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else:
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return pre_bias
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else:
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inputs = {'x': x, 'kernel': weight}
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attrs = {
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'paddings': padding,
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'dilations': dilation,
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'strides': stride,
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'groups': groups,
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'subm': subm,
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'key': key,
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}
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op_type = 'sparse_conv3d_implicit_gemm'
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helper = LayerHelper(op_type, **locals())
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pre_bias = helper.create_sparse_variable_for_type_inference(x.dtype)
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outputs = {"out": pre_bias}
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helper.append_op(
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type=op_type, inputs=inputs, outputs=outputs, attrs=attrs
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)
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if bias is not None:
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return add(pre_bias, bias)
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else:
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return pre_bias
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def _conv2d_igemm(
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2024-07-10 13:51:34 +08:00
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x: Tensor,
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weight: Tensor,
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bias: Tensor | None = None,
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stride: Size2 = 1,
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padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0,
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dilation: Size2 = 1,
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groups: Literal[1] = 1,
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subm: Literal[True] = True,
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key: str | None = None,
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data_format: Literal["NHWC"] = "NHWC",
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name: str | None = None,
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) -> Tensor:
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2024-04-22 13:33:02 +08:00
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assert groups == 1, "Currently, only support groups=1"
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assert subm is True, "Currently, only support subm=True for implicit gemm"
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dims = 2
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|
|
# Currently, only support 'NDHWC'
|
|
|
|
|
if data_format not in ["NHWC"]:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
"Attr(data_format) should be 'NHWC'. Received "
|
|
|
|
|
f"Attr(data_format): {data_format}."
|
|
|
|
|
)
|
|
|
|
|
if len(x.shape) != 4:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
f"Input x should be 5D tensor, but received x with the shape of {x.shape}"
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
channel_last = data_format == "NHWC"
|
|
|
|
|
channel_dim = -1 if channel_last else 1
|
|
|
|
|
if len(x.shape) != 4:
|
|
|
|
|
raise ValueError(
|
|
|
|
|
f"Input x should be 4D tensor, but received x with the shape of {x.shape}"
|
|
|
|
|
)
|
|
|
|
|
num_channels = x.shape[channel_dim]
|
|
|
|
|
|
|
|
|
|
padding, padding_algorithm = _update_padding_nd(padding, channel_last, dims)
|
|
|
|
|
stride = convert_to_list(stride, dims, 'stride')
|
|
|
|
|
dilation = convert_to_list(dilation, dims, 'dilation')
|
|
|
|
|
|
2024-08-16 14:21:19 +08:00
|
|
|
if in_dynamic_or_pir_mode():
|
2024-04-22 13:33:02 +08:00
|
|
|
pre_bias = _C_ops.sparse_conv3d_implicit_gemm(
|
|
|
|
|
x,
|
|
|
|
|
weight,
|
|
|
|
|
padding,
|
|
|
|
|
dilation,
|
|
|
|
|
stride,
|
|
|
|
|
groups,
|
|
|
|
|
subm,
|
|
|
|
|
key if key is not None else "",
|
|
|
|
|
)
|
|
|
|
|
if bias is not None:
|
|
|
|
|
return add(pre_bias, bias)
|
|
|
|
|
else:
|
|
|
|
|
return pre_bias
|
|
|
|
|
else:
|
|
|
|
|
inputs = {'x': x, 'kernel': weight}
|
|
|
|
|
attrs = {
|
|
|
|
|
'paddings': padding,
|
|
|
|
|
'dilations': dilation,
|
|
|
|
|
'strides': stride,
|
|
|
|
|
'groups': groups,
|
|
|
|
|
'subm': subm,
|
|
|
|
|
'key': key,
|
|
|
|
|
}
|
|
|
|
|
op_type = 'sparse_conv3d_implicit_gemm'
|
|
|
|
|
helper = LayerHelper(op_type, **locals())
|
|
|
|
|
pre_bias = helper.create_sparse_variable_for_type_inference(x.dtype)
|
|
|
|
|
outputs = {"out": pre_bias}
|
|
|
|
|
helper.append_op(
|
|
|
|
|
type=op_type, inputs=inputs, outputs=outputs, attrs=attrs
|
|
|
|
|
)
|
|
|
|
|
if bias is not None:
|
|
|
|
|
return add(pre_bias, bias)
|
|
|
|
|
else:
|
|
|
|
|
return pre_bias
|
|
|
|
|
|
|
|
|
|
|
2022-10-23 20:01:27 +08:00
|
|
|
def conv3d(
|
2024-07-10 13:51:34 +08:00
|
|
|
x: Tensor,
|
|
|
|
|
weight: Tensor,
|
|
|
|
|
bias: Tensor | None = None,
|
|
|
|
|
stride: Size3 = 1,
|
|
|
|
|
padding: _PaddingSizeMode | Size3 | Size6 | Sequence[Size2] = 0,
|
|
|
|
|
dilation: Size3 = 1,
|
|
|
|
|
groups: Literal[1] = 1,
|
|
|
|
|
data_format: DataLayout3D = "NDHWC",
|
|
|
|
|
name: str | None = None,
|
|
|
|
|
) -> Tensor:
|
2022-04-15 11:38:24 +08:00
|
|
|
r"""
|
|
|
|
|
The sparse convolution3d functional calculates the output based on the input, filter
|
|
|
|
|
and strides, paddings, dilations, groups parameters. Input(Input) and
|
2022-09-14 21:56:19 +08:00
|
|
|
Output(Output) are multidimensional SparseCooTensors with a shape of
|
2022-04-15 11:38:24 +08:00
|
|
|
:math:`[N, D, H, W, C]` . Where N is batch size, C is the number of
|
|
|
|
|
channels, D is the depth of the feature, H is the height of the feature,
|
2022-09-14 21:56:19 +08:00
|
|
|
and W is the width of the feature. If bias attribution is provided,
|
|
|
|
|
bias is added to the output of the convolution.
|
2022-04-15 11:38:24 +08:00
|
|
|
|
|
|
|
|
For each input :math:`X`, the equation is:
|
|
|
|
|
|
|
|
|
|
.. math::
|
|
|
|
|
|
|
|
|
|
Out = \sigma (W \ast X + b)
|
|
|
|
|
|
|
|
|
|
In the above equation:
|
|
|
|
|
|
|
|
|
|
* :math:`X`: Input value, a tensor with NCDHW or NDHWC format.
|
|
|
|
|
* :math:`W`: Filter value, a tensor with MCDHW format.
|
|
|
|
|
* :math:`\\ast`: Convolution operation.
|
|
|
|
|
* :math:`b`: Bias value, a 1-D tensor with shape [M].
|
|
|
|
|
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
|
|
|
|
|
|
|
|
|
|
Args:
|
2022-09-14 21:56:19 +08:00
|
|
|
x (Tensor): The input is 5-D SparseCooTensor with shape [N, D, H, W, C], the data
|
2022-04-15 11:38:24 +08:00
|
|
|
type of input is float16 or float32 or float64.
|
|
|
|
|
weight (Tensor): The convolution kernel, a Tensor with shape [kD, kH, kW, C/g, M],
|
|
|
|
|
where M is the number of filters(output channels), g is the number of groups,
|
|
|
|
|
kD, kH, kW are the filter's depth, height and width respectively.
|
2024-07-10 13:51:34 +08:00
|
|
|
bias (Tensor|None, optional): The bias, a Tensor of shape [M].
|
2022-10-24 18:11:01 +08:00
|
|
|
stride (int|list|tuple, optional): The stride size. It means the stride in convolution. If stride is a
|
2022-09-14 21:56:19 +08:00
|
|
|
list/tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
|
2022-04-15 11:38:24 +08:00
|
|
|
Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
|
2022-10-24 18:11:01 +08:00
|
|
|
padding (string|int|list|tuple, optional): The padding size. It means the number of zero-paddings
|
2022-04-15 11:38:24 +08:00
|
|
|
on both sides for each dimension. If `padding` is a string, either 'VALID' or
|
|
|
|
|
'SAME' which is the padding algorithm. If padding size is a tuple or list,
|
|
|
|
|
it could be in three forms: `[pad_depth, pad_height, pad_width]` or
|
|
|
|
|
`[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
|
|
|
|
|
and when `data_format` is `"NCDHW"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
|
|
|
|
|
when `data_format` is `"NDHWC"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
|
|
|
|
|
Default: padding = 0.
|
2022-10-24 18:11:01 +08:00
|
|
|
dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel points.
|
2022-04-15 11:38:24 +08:00
|
|
|
If dilation is a list/tuple, it must contain three integers, (dilation_depth, dilation_height,
|
2022-09-14 21:56:19 +08:00
|
|
|
dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
|
2022-04-15 11:38:24 +08:00
|
|
|
Default: dilation = 1.
|
2022-10-24 18:11:01 +08:00
|
|
|
groups (int, optional): The groups number of the Conv3D Layer. According to grouped
|
2022-04-15 11:38:24 +08:00
|
|
|
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
|
|
|
|
|
the first half of the filters is only connected to the first half
|
|
|
|
|
of the input channels, while the second half of the filters is only
|
|
|
|
|
connected to the second half of the input channels. Default: groups=1. Currently, only support groups=1.
|
2022-09-14 21:56:19 +08:00
|
|
|
data_format (str, optional): Specify the data format of the input, and the data format of the output
|
2022-04-15 11:38:24 +08:00
|
|
|
will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`.
|
|
|
|
|
The default is `"NDHWC"`. When it is `"NDHWC"`, the data is stored in the order of:
|
|
|
|
|
`[batch_size, input_depth, input_height, input_width, input_channels]`.
|
2024-07-10 13:51:34 +08:00
|
|
|
name(str|None, optional): For detailed information, please refer
|
2022-09-14 21:56:19 +08:00
|
|
|
to :ref:`api_guide_Name`. Usually name is no need to set and
|
2022-04-15 11:38:24 +08:00
|
|
|
None by default.
|
|
|
|
|
|
|
|
|
|
Returns:
|
2022-09-14 21:56:19 +08:00
|
|
|
A SparseCooTensor representing the conv3d, whose data type is the same with input.
|
2022-04-15 11:38:24 +08:00
|
|
|
|
|
|
|
|
Examples:
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
.. code-block:: pycon
|
2022-04-15 11:38:24 +08:00
|
|
|
|
2023-09-10 22:42:23 -05:00
|
|
|
>>> import paddle
|
|
|
|
|
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
>>> indices = [
|
|
|
|
|
... [0, 0, 0, 0],
|
|
|
|
|
... [0, 0, 0, 0],
|
|
|
|
|
... [0, 0, 1, 2],
|
|
|
|
|
... [1, 3, 2, 3],
|
|
|
|
|
... ]
|
2023-09-10 22:42:23 -05:00
|
|
|
>>> values = [[1], [2], [3], [4]]
|
|
|
|
|
>>> indices = paddle.to_tensor(indices, dtype='int32')
|
|
|
|
|
>>> values = paddle.to_tensor(values, dtype='float32')
|
|
|
|
|
>>> dense_shape = [1, 1, 3, 4, 1]
|
|
|
|
|
>>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
|
|
|
|
|
>>> weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
|
|
|
|
|
>>> y = paddle.sparse.nn.functional.conv3d(sparse_x, weight)
|
|
|
|
|
>>> print(y.shape)
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
paddle.Size([1, 1, 1, 2, 1])
|
2022-04-15 11:38:24 +08:00
|
|
|
"""
|
2022-10-23 20:01:27 +08:00
|
|
|
return _conv3d(
|
|
|
|
|
x,
|
|
|
|
|
weight,
|
|
|
|
|
bias,
|
|
|
|
|
stride,
|
|
|
|
|
padding,
|
|
|
|
|
dilation,
|
|
|
|
|
groups,
|
|
|
|
|
False,
|
|
|
|
|
None,
|
|
|
|
|
data_format,
|
|
|
|
|
name,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def subm_conv3d(
|
2024-07-10 13:51:34 +08:00
|
|
|
x: Tensor,
|
|
|
|
|
weight: Tensor,
|
|
|
|
|
bias: Tensor | None = None,
|
|
|
|
|
stride: Size3 = 1,
|
|
|
|
|
padding: _PaddingSizeMode | Size3 | Size6 | Sequence[Size2] = 0,
|
|
|
|
|
dilation: Size3 = 1,
|
|
|
|
|
groups: Literal[1] = 1,
|
|
|
|
|
data_format: DataLayout3D = "NDHWC",
|
|
|
|
|
key: str | None = None,
|
|
|
|
|
name: str | None = None,
|
|
|
|
|
) -> Tensor:
|
2022-04-15 11:38:24 +08:00
|
|
|
r"""
|
|
|
|
|
|
|
|
|
|
The sparse submanifold convolution3d functional calculates the output based on the input, filter
|
|
|
|
|
and strides, paddings, dilations, groups parameters. Input(Input) and
|
2022-09-14 21:56:19 +08:00
|
|
|
Output(Output) are multidimensional SparseCooTensors with a shape of
|
2022-04-15 11:38:24 +08:00
|
|
|
:math:`[N, D, H, W, C]` . Where N is batch size, C is the number of
|
|
|
|
|
channels, D is the depth of the feature, H is the height of the feature,
|
2022-09-14 21:56:19 +08:00
|
|
|
and W is the width of the feature. If bias attribution is provided,
|
|
|
|
|
bias is added to the output of the convolution.
|
2022-04-15 11:38:24 +08:00
|
|
|
|
|
|
|
|
For each input :math:`X`, the equation is:
|
|
|
|
|
|
|
|
|
|
.. math::
|
|
|
|
|
|
|
|
|
|
Out = W \ast X + b
|
|
|
|
|
|
|
|
|
|
In the above equation:
|
|
|
|
|
|
|
|
|
|
* :math:`X`: Input value, a tensor with NCDHW or NDHWC format.
|
|
|
|
|
* :math:`W`: Filter value, a tensor with DHWCM format.
|
|
|
|
|
* :math:`\\ast`: Submanifold Convolution operation, refer to the paper: https://arxiv.org/abs/1706.01307.
|
|
|
|
|
* :math:`b`: Bias value, a 1-D tensor with shape [M].
|
|
|
|
|
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
|
|
|
|
|
|
|
|
|
|
Args:
|
2022-09-14 21:56:19 +08:00
|
|
|
x (Tensor): The input is 5-D SparseCooTensor with shape [N, D, H, W, C], the data
|
2022-04-15 11:38:24 +08:00
|
|
|
type of input is float16 or float32 or float64.
|
|
|
|
|
weight (Tensor): The convolution kernel, a Tensor with shape [kD, kH, kW, C/g, M],
|
|
|
|
|
where M is the number of filters(output channels), g is the number of groups,
|
|
|
|
|
kD, kH, kW are the filter's depth, height and width respectively.
|
2024-07-10 13:51:34 +08:00
|
|
|
bias (Tensor|None, optional): The bias, a Tensor of shape [M].
|
2022-10-24 18:11:01 +08:00
|
|
|
stride (int|list|tuple, optional): The stride size. It means the stride in convolution. If stride is a
|
2022-09-14 21:56:19 +08:00
|
|
|
list/tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
|
2022-04-15 11:38:24 +08:00
|
|
|
Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
|
2022-09-14 21:56:19 +08:00
|
|
|
padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
|
2022-04-15 11:38:24 +08:00
|
|
|
on both sides for each dimension. If `padding` is a string, either 'VALID' or
|
|
|
|
|
'SAME' which is the padding algorithm. If padding size is a tuple or list,
|
|
|
|
|
it could be in three forms: `[pad_depth, pad_height, pad_width]` or
|
|
|
|
|
`[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
|
|
|
|
|
and when `data_format` is `"NCDHW"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
|
|
|
|
|
when `data_format` is `"NHWC"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
|
|
|
|
|
Default: padding = 0.
|
2022-10-24 18:11:01 +08:00
|
|
|
dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel points.
|
2022-04-15 11:38:24 +08:00
|
|
|
If dilation is a list/tuple, it must contain three integers, (dilation_depth, dilation_height,
|
2022-09-14 21:56:19 +08:00
|
|
|
dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
|
2022-04-15 11:38:24 +08:00
|
|
|
Default: dilation = 1.
|
2022-10-24 18:11:01 +08:00
|
|
|
groups (int, optional): The groups number of the Conv3D Layer. According to grouped
|
2022-04-15 11:38:24 +08:00
|
|
|
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
|
|
|
|
|
the first half of the filters is only connected to the first half
|
|
|
|
|
of the input channels, while the second half of the filters is only
|
|
|
|
|
connected to the second half of the input channels. Currently, only support groups=1.
|
2022-09-14 21:56:19 +08:00
|
|
|
data_format (str, optional): Specify the data format of the input, and the data format of the output
|
2022-04-15 11:38:24 +08:00
|
|
|
will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`.
|
|
|
|
|
The default is `"NDHWC"`. When it is `"NDHWC"`, the data is stored in the order of:
|
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|
|
|
`[batch_size, input_depth, input_height, input_width, input_channels]`.
|
2024-07-10 13:51:34 +08:00
|
|
|
key(str|None, optional): the key is used to save or use the same rulebook,
|
2022-07-26 20:13:27 +08:00
|
|
|
the definition and role of rulebook refers to
|
2022-09-14 21:56:19 +08:00
|
|
|
https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf. The
|
2022-07-26 20:13:27 +08:00
|
|
|
default value is None.
|
2024-07-10 13:51:34 +08:00
|
|
|
name(str|None, optional): For detailed information, please refer
|
2022-09-14 21:56:19 +08:00
|
|
|
to :ref:`api_guide_Name`. Usually name is no need to set and
|
2022-04-15 11:38:24 +08:00
|
|
|
None by default.
|
|
|
|
|
|
|
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|
Returns:
|
2022-09-14 21:56:19 +08:00
|
|
|
A SparseCooTensor representing the conv3d, whose data type is
|
|
|
|
|
the same with input.
|
2022-04-15 11:38:24 +08:00
|
|
|
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|
|
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|
Examples:
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
.. code-block:: pycon
|
2022-04-15 11:38:24 +08:00
|
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2023-09-10 22:42:23 -05:00
|
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|
>>> import paddle
|
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[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
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>>> indices = [
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... [0, 0, 0, 0],
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... [0, 0, 0, 0],
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... [0, 0, 1, 2],
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... [1, 3, 2, 3],
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... ]
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2023-09-10 22:42:23 -05:00
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>>> values = [[1], [2], [3], [4]]
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>>> indices = paddle.to_tensor(indices, dtype='int32')
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>>> values = paddle.to_tensor(values, dtype='float32')
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>>> dense_shape = [1, 1, 3, 4, 1]
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>>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
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>>> weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
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|
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|
>>> y = paddle.sparse.nn.functional.subm_conv3d(sparse_x, weight)
|
|
|
|
|
>>> print(y.shape)
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
paddle.Size([1, 1, 3, 4, 1])
|
2022-04-15 11:38:24 +08:00
|
|
|
"""
|
2022-10-23 20:01:27 +08:00
|
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|
return _conv3d(
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|
x,
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|
weight,
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|
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|
bias,
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|
|
|
stride,
|
|
|
|
|
padding,
|
|
|
|
|
dilation,
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|
|
|
groups,
|
|
|
|
|
True,
|
|
|
|
|
key,
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|
|
|
|
data_format,
|
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|
|
|
name,
|
|
|
|
|
)
|
2023-06-01 21:58:31 +08:00
|
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|
2024-04-22 13:33:02 +08:00
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def subm_conv3d_igemm(
|
2024-07-10 13:51:34 +08:00
|
|
|
x: Tensor,
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weight: Tensor,
|
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|
bias: Tensor | None = None,
|
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|
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stride: Size3 = 1,
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padding: _PaddingSizeMode | Size3 | Size6 | Sequence[Size2] = 0,
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dilation: Size3 = 1,
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groups: Literal[1] = 1,
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data_format: DataLayout3D = "NDHWC",
|
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|
|
|
key: str | None = None,
|
|
|
|
|
name: str | None = None,
|
|
|
|
|
) -> Tensor:
|
2024-04-22 13:33:02 +08:00
|
|
|
r"""
|
|
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|
|
|
|
|
|
|
The sparse submanifold convolution3d functional calculates the output based on the input, filter
|
|
|
|
|
and strides, paddings, dilations, groups parameters. Input(Input) and
|
|
|
|
|
Output(Output) are multidimensional SparseCooTensors with a shape of
|
|
|
|
|
:math:`[N, D, H, W, C]` . Where N is batch size, C is the number of
|
|
|
|
|
channels, D is the depth of the feature, H is the height of the feature,
|
|
|
|
|
and W is the width of the feature. If bias attribution is provided,
|
|
|
|
|
bias is added to the output of the convolution.
|
|
|
|
|
|
|
|
|
|
For each input :math:`X`, the equation is:
|
|
|
|
|
|
|
|
|
|
.. math::
|
|
|
|
|
|
|
|
|
|
Out = W \ast X + b
|
|
|
|
|
|
|
|
|
|
In the above equation:
|
|
|
|
|
|
|
|
|
|
* :math:`X`: Input value, a tensor with NCDHW or NDHWC format.
|
|
|
|
|
* :math:`W`: Filter value, a tensor with DHWCM format.
|
|
|
|
|
* :math:`\\ast`: Submanifold Convolution operation, refer to the paper: https://arxiv.org/abs/1706.01307.
|
|
|
|
|
* :math:`b`: Bias value, a 1-D tensor with shape [M].
|
|
|
|
|
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
x (Tensor): The input is 5-D SparseCooTensor with shape [N, D, H, W, C], the data
|
|
|
|
|
type of input is float16 or float32 or float64.
|
|
|
|
|
weight (Tensor): The convolution kernel, a Tensor with shape [kD, kH, kW, C/g, M],
|
|
|
|
|
where M is the number of filters(output channels), g is the number of groups,
|
|
|
|
|
kD, kH, kW are the filter's depth, height and width respectively.
|
2024-07-10 13:51:34 +08:00
|
|
|
bias (Tensor|None, optional): The bias, a Tensor of shape [M].
|
2024-04-22 13:33:02 +08:00
|
|
|
stride (int|list|tuple, optional): The stride size. It means the stride in convolution. If stride is a
|
|
|
|
|
list/tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
|
|
|
|
|
Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
|
|
|
|
|
padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
|
|
|
|
|
on both sides for each dimension. If `padding` is a string, either 'VALID' or
|
|
|
|
|
'SAME' which is the padding algorithm. If padding size is a tuple or list,
|
|
|
|
|
it could be in three forms: `[pad_depth, pad_height, pad_width]` or
|
|
|
|
|
`[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
|
|
|
|
|
and when `data_format` is `"NCDHW"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
|
|
|
|
|
when `data_format` is `"NHWC"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
|
|
|
|
|
Default: padding = 0.
|
|
|
|
|
dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel points.
|
|
|
|
|
If dilation is a list/tuple, it must contain three integers, (dilation_depth, dilation_height,
|
|
|
|
|
dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
|
|
|
|
|
Default: dilation = 1.
|
|
|
|
|
groups (int, optional): The groups number of the Conv3D Layer. According to grouped
|
|
|
|
|
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
|
|
|
|
|
the first half of the filters is only connected to the first half
|
|
|
|
|
of the input channels, while the second half of the filters is only
|
|
|
|
|
connected to the second half of the input channels. Currently, only support groups=1.
|
|
|
|
|
data_format (str, optional): Specify the data format of the input, and the data format of the output
|
|
|
|
|
will be consistent with that of the input. An optional string from: `"NCDHW"`, `"NDHWC"`.
|
|
|
|
|
The default is `"NDHWC"`. When it is `"NDHWC"`, the data is stored in the order of:
|
|
|
|
|
`[batch_size, input_depth, input_height, input_width, input_channels]`.
|
2024-07-10 13:51:34 +08:00
|
|
|
key(str|None, optional): the key is used to save or use the same rulebook,
|
2024-04-22 13:33:02 +08:00
|
|
|
the definition and role of rulebook refers to
|
|
|
|
|
https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf. The
|
|
|
|
|
default value is None.
|
2024-07-10 13:51:34 +08:00
|
|
|
name(str|None, optional): For detailed information, please refer
|
2024-04-22 13:33:02 +08:00
|
|
|
to :ref:`api_guide_Name`. Usually name is no need to set and
|
|
|
|
|
None by default.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
A SparseCooTensor representing the conv3d, whose data type is
|
|
|
|
|
the same with input.
|
|
|
|
|
|
|
|
|
|
Examples:
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
.. code-block:: pycon
|
2024-04-22 13:33:02 +08:00
|
|
|
|
|
|
|
|
>>> import paddle
|
|
|
|
|
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
>>> indices = [
|
|
|
|
|
... [0, 0, 0, 0],
|
|
|
|
|
... [0, 0, 0, 0],
|
|
|
|
|
... [0, 0, 1, 2],
|
|
|
|
|
... [1, 3, 2, 3],
|
|
|
|
|
... ]
|
2024-04-22 13:33:02 +08:00
|
|
|
>>> values = [[1], [2], [3], [4]]
|
|
|
|
|
>>> indices = paddle.to_tensor(indices, dtype='int32')
|
|
|
|
|
>>> values = paddle.to_tensor(values, dtype='float32')
|
|
|
|
|
>>> dense_shape = [1, 1, 3, 4, 1]
|
|
|
|
|
>>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
|
|
|
|
|
>>> weight = paddle.randn((1, 3, 3, 1, 1), dtype='float32')
|
|
|
|
|
>>> y = paddle.sparse.nn.functional.subm_conv3d(sparse_x, weight)
|
|
|
|
|
>>> print(y.shape)
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
paddle.Size([1, 1, 3, 4, 1])
|
2024-04-22 13:33:02 +08:00
|
|
|
"""
|
|
|
|
|
return _conv3d_igemm(
|
|
|
|
|
x,
|
|
|
|
|
weight,
|
|
|
|
|
bias,
|
|
|
|
|
stride,
|
|
|
|
|
padding,
|
|
|
|
|
dilation,
|
|
|
|
|
groups,
|
|
|
|
|
True,
|
|
|
|
|
key,
|
|
|
|
|
data_format,
|
|
|
|
|
name,
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
|
2023-06-01 21:58:31 +08:00
|
|
|
def conv2d(
|
2024-07-10 13:51:34 +08:00
|
|
|
x: Tensor,
|
|
|
|
|
weight: Tensor,
|
|
|
|
|
bias: Tensor | None = None,
|
|
|
|
|
stride: Size2 = 1,
|
|
|
|
|
padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0,
|
|
|
|
|
dilation: Size2 = 1,
|
|
|
|
|
groups: Literal[1] = 1,
|
|
|
|
|
data_format: DataLayout2D = "NHWC",
|
|
|
|
|
name: str | None = None,
|
|
|
|
|
) -> Tensor:
|
2023-06-01 21:58:31 +08:00
|
|
|
r"""
|
|
|
|
|
|
|
|
|
|
The sparse convolution2d functional calculates the output based on the input, filter
|
|
|
|
|
and strides, paddings, dilations, groups parameters. Input(Input) and
|
|
|
|
|
Output(Output) are multidimensional SparseCooTensors with a shape of
|
|
|
|
|
:math:`[N, H, W, C]` . Where N is batch size, C is the number of
|
|
|
|
|
channels, H is the height of the feature,
|
|
|
|
|
and W is the width of the feature. If bias attribution is provided,
|
|
|
|
|
bias is added to the output of the convolution.
|
|
|
|
|
|
|
|
|
|
For each input :math:`X`, the equation is:
|
|
|
|
|
|
|
|
|
|
.. math::
|
|
|
|
|
|
|
|
|
|
Out = \sigma (W \ast X + b)
|
|
|
|
|
|
|
|
|
|
In the above equation:
|
|
|
|
|
|
|
|
|
|
* :math:`X`: Input value, a tensor with NHWC format.
|
|
|
|
|
* :math:`W`: Filter value, a tensor with HWCM format.
|
|
|
|
|
* :math:`\\ast`: Convolution operation.
|
|
|
|
|
* :math:`b`: Bias value, a 1-D tensor with shape [M].
|
|
|
|
|
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
x (Tensor): The input is 4-D SparseCooTensor with shape [N, H, W, C], the data
|
|
|
|
|
type of input is float16 or float32 or float64.
|
|
|
|
|
weight (Tensor): The convolution kernel, a Tensor with shape [kH, kW, C/g, M],
|
|
|
|
|
where M is the number of filters(output channels), g is the number of groups,
|
|
|
|
|
kD, kH, kW are the filter's height and width respectively.
|
2024-07-10 13:51:34 +08:00
|
|
|
bias (Tensor|None, optional): The bias, a Tensor of shape [M].
|
2023-06-01 21:58:31 +08:00
|
|
|
stride (int|list|tuple, optional): The stride size. It means the stride in convolution. If stride is a
|
|
|
|
|
list/tuple, it must contain two integers, (stride_height, stride_width).
|
|
|
|
|
Otherwise, stride_height = stride_width = stride. Default: stride = 1.
|
|
|
|
|
padding (string|int|list|tuple, optional): The padding size. It means the number of zero-paddings
|
|
|
|
|
on both sides for each dimension. If `padding` is a string, either 'VALID' or
|
|
|
|
|
'SAME' which is the padding algorithm. If padding size is a tuple or list,
|
|
|
|
|
it could be in three forms: `[pad_height, pad_width]` or
|
|
|
|
|
`[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
|
|
|
|
|
when `data_format` is `"NHWC"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
|
|
|
|
|
Default: padding = 0.
|
|
|
|
|
dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel points.
|
|
|
|
|
If dilation is a list/tuple, it must contain two integers, (dilation_height,
|
|
|
|
|
dilation_width). Otherwise, dilation_height = dilation_width = dilation.
|
|
|
|
|
Default: dilation = 1.
|
|
|
|
|
groups (int, optional): The groups number of the Conv2D Layer. According to grouped
|
|
|
|
|
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
|
|
|
|
|
the first half of the filters is only connected to the first half
|
|
|
|
|
of the input channels, while the second half of the filters is only
|
|
|
|
|
connected to the second half of the input channels. Default: groups=1. Currently, only support groups=1.
|
|
|
|
|
data_format (str, optional): Specify the data format of the input, and the data format of the output
|
|
|
|
|
will be consistent with that of the input. An optional string from: `"NHWC"`.
|
|
|
|
|
The default is `"NHWC"`. When it is `"NHWC"`, the data is stored in the order of:
|
|
|
|
|
`[batch_size, input_height, input_width, input_channels]`.
|
2024-07-10 13:51:34 +08:00
|
|
|
name(str|None, optional): For detailed information, please refer
|
2023-06-01 21:58:31 +08:00
|
|
|
to :ref:`api_guide_Name`. Usually name is no need to set and
|
|
|
|
|
None by default.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
A SparseCooTensor representing the conv2d, whose data type is the same with input.
|
|
|
|
|
|
|
|
|
|
Examples:
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
.. code-block:: pycon
|
2023-06-01 21:58:31 +08:00
|
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|
|
2023-09-10 22:42:23 -05:00
|
|
|
>>> import paddle
|
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|
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|
|
|
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|
>>> indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
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>>> values = [[1], [2], [3], [4]]
|
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>>> indices = paddle.to_tensor(indices, dtype='int32')
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>>> values = paddle.to_tensor(values, dtype='float32')
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>>> dense_shape = [1, 3, 4, 1]
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>>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
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>>> weight = paddle.randn((3, 3, 1, 1), dtype='float32')
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>>> y = paddle.sparse.nn.functional.conv2d(sparse_x, weight)
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>>> print(y.shape)
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
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paddle.Size([1, 1, 2, 1])
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2023-06-01 21:58:31 +08:00
|
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|
"""
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return _conv2d(
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x,
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weight,
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bias,
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|
stride,
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padding,
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dilation,
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|
|
|
groups,
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|
False,
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None,
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|
data_format,
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|
name,
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)
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def subm_conv2d(
|
2024-07-10 13:51:34 +08:00
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x: Tensor,
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weight: Tensor,
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|
bias: Tensor | None = None,
|
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|
stride: Size2 = 1,
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padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0,
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dilation: Size2 = 1,
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groups: Literal[1] = 1,
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data_format: DataLayout2D = "NHWC",
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|
key: str | None = None,
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name: str | None = None,
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|
) -> Tensor:
|
2023-06-01 21:58:31 +08:00
|
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|
r"""
|
|
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The sparse submanifold convolution2d functional calculates the output based on the input, filter
|
|
|
|
|
and strides, paddings, dilations, groups parameters. Input(Input) and
|
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|
Output(Output) are multidimensional SparseCooTensors with a shape of
|
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|
|
|
:math:`[N, H, W, C]` . Where N is batch size, C is the number of
|
|
|
|
|
channels, H is the height of the feature,
|
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|
|
and W is the width of the feature. If bias attribution is provided,
|
|
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|
|
bias is added to the output of the convolution.
|
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|
|
|
|
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|
For each input :math:`X`, the equation is:
|
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.. math::
|
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Out = \sigma (W \ast X + b)
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|
|
In the above equation:
|
|
|
|
|
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|
|
* :math:`X`: Input value, a tensor with NHWC format.
|
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|
* :math:`W`: Filter value, a tensor with HWCM format.
|
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|
|
|
* :math:`\\ast`: Submanifold Convolution operation, refer to the paper: https://arxiv.org/abs/1706.01307.
|
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|
* :math:`b`: Bias value, a 1-D tensor with shape [M].
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|
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
|
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|
|
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|
|
|
Args:
|
|
|
|
|
x (Tensor): The input is 4-D SparseCooTensor with shape [N, H, W, C], the data
|
|
|
|
|
type of input is float16 or float32 or float64.
|
|
|
|
|
weight (Tensor): The convolution kernel, a Tensor with shape [kH, kW, C/g, M],
|
|
|
|
|
where M is the number of filters(output channels), g is the number of groups,
|
|
|
|
|
kD, kH, kW are the filter's height and width respectively.
|
2024-07-10 13:51:34 +08:00
|
|
|
bias (Tensor|None, optional): The bias, a Tensor of shape [M].
|
2023-06-01 21:58:31 +08:00
|
|
|
stride (int|list|tuple, optional): The stride size. It means the stride in convolution. If stride is a
|
|
|
|
|
list/tuple, it must contain two integers, (stride_height, stride_width).
|
|
|
|
|
Otherwise, stride_height = stride_width = stride. Default: stride = 1.
|
|
|
|
|
padding (string|int|list|tuple, optional): The padding size. It means the number of zero-paddings
|
|
|
|
|
on both sides for each dimension. If `padding` is a string, either 'VALID' or
|
|
|
|
|
'SAME' which is the padding algorithm. If padding size is a tuple or list,
|
|
|
|
|
it could be in three forms: `[pad_height, pad_width]` or
|
|
|
|
|
`[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
|
|
|
|
|
when `data_format` is `"NHWC"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
|
|
|
|
|
Default: padding = 0.
|
|
|
|
|
dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel points.
|
|
|
|
|
If dilation is a list/tuple, it must contain two integers, (dilation_height,
|
|
|
|
|
dilation_width). Otherwise, dilation_height = dilation_width = dilation.
|
|
|
|
|
Default: dilation = 1.
|
|
|
|
|
groups (int, optional): The groups number of the Conv2D Layer. According to grouped
|
|
|
|
|
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
|
|
|
|
|
the first half of the filters is only connected to the first half
|
|
|
|
|
of the input channels, while the second half of the filters is only
|
|
|
|
|
connected to the second half of the input channels. Default: groups=1. Currently, only support groups=1.
|
|
|
|
|
data_format (str, optional): Specify the data format of the input, and the data format of the output
|
|
|
|
|
will be consistent with that of the input. An optional string from: `"NHWC"`.
|
|
|
|
|
The default is `"NHWC"`. When it is `"NHWC"`, the data is stored in the order of:
|
|
|
|
|
`[batch_size, input_height, input_width, input_channels]`.
|
2024-07-10 13:51:34 +08:00
|
|
|
key(str|None, optional): the key is used to save or use the same rulebook,
|
2023-06-01 21:58:31 +08:00
|
|
|
the definition and role of rulebook refers to
|
|
|
|
|
https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf. The
|
|
|
|
|
default value is None.
|
2024-07-10 13:51:34 +08:00
|
|
|
name(str|None, optional): For detailed information, please refer
|
2023-06-01 21:58:31 +08:00
|
|
|
to :ref:`api_guide_Name`. Usually name is no need to set and
|
|
|
|
|
None by default.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
A SparseCooTensor representing the conv2d, whose data type is the same with input.
|
|
|
|
|
|
|
|
|
|
Examples:
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
.. code-block:: pycon
|
2023-06-01 21:58:31 +08:00
|
|
|
|
2023-09-10 22:42:23 -05:00
|
|
|
>>> import paddle
|
|
|
|
|
|
|
|
|
|
>>> indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
|
|
|
|
|
>>> values = [[1], [2], [3], [4]]
|
|
|
|
|
>>> indices = paddle.to_tensor(indices, dtype='int32')
|
|
|
|
|
>>> values = paddle.to_tensor(values, dtype='float32')
|
|
|
|
|
>>> dense_shape = [1, 3, 4, 1]
|
|
|
|
|
>>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
|
|
|
|
|
>>> weight = paddle.randn((3, 3, 1, 1), dtype='float32')
|
|
|
|
|
>>> y = paddle.sparse.nn.functional.subm_conv2d(sparse_x, weight)
|
|
|
|
|
>>> print(y.shape)
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
paddle.Size([1, 3, 4, 1])
|
2023-06-01 21:58:31 +08:00
|
|
|
"""
|
|
|
|
|
return _conv2d(
|
|
|
|
|
x,
|
|
|
|
|
weight,
|
|
|
|
|
bias,
|
|
|
|
|
stride,
|
|
|
|
|
padding,
|
|
|
|
|
dilation,
|
|
|
|
|
groups,
|
|
|
|
|
True,
|
|
|
|
|
key,
|
|
|
|
|
data_format,
|
|
|
|
|
name,
|
|
|
|
|
)
|
2024-04-22 13:33:02 +08:00
|
|
|
|
|
|
|
|
|
|
|
|
|
def subm_conv2d_igemm(
|
2024-07-10 13:51:34 +08:00
|
|
|
x: Tensor,
|
|
|
|
|
weight: Tensor,
|
|
|
|
|
bias: Tensor | None = None,
|
|
|
|
|
stride: Size2 = 1,
|
|
|
|
|
padding: _PaddingSizeMode | Size2 | Size4 | Sequence[Size2] = 0,
|
|
|
|
|
dilation: Size2 = 1,
|
|
|
|
|
groups: Literal[1] = 1,
|
|
|
|
|
data_format: DataLayout2D = "NHWC",
|
|
|
|
|
key: str | None = None,
|
|
|
|
|
name: str | None = None,
|
|
|
|
|
) -> Tensor:
|
2024-04-22 13:33:02 +08:00
|
|
|
r"""
|
|
|
|
|
|
|
|
|
|
The sparse submanifold convolution2d functional calculates the output based on the input, filter
|
|
|
|
|
and strides, paddings, dilations, groups parameters. Input(Input) and
|
|
|
|
|
Output(Output) are multidimensional SparseCooTensors with a shape of
|
|
|
|
|
:math:`[N, H, W, C]` . Where N is batch size, C is the number of
|
|
|
|
|
channels, H is the height of the feature,
|
|
|
|
|
and W is the width of the feature. If bias attribution is provided,
|
|
|
|
|
bias is added to the output of the convolution.
|
|
|
|
|
|
|
|
|
|
For each input :math:`X`, the equation is:
|
|
|
|
|
|
|
|
|
|
.. math::
|
|
|
|
|
|
|
|
|
|
Out = \sigma (W \ast X + b)
|
|
|
|
|
|
|
|
|
|
In the above equation:
|
|
|
|
|
|
|
|
|
|
* :math:`X`: Input value, a tensor with NHWC format.
|
|
|
|
|
* :math:`W`: Filter value, a tensor with HWCM format.
|
|
|
|
|
* :math:`\\ast`: Submanifold Convolution operation, refer to the paper: https://arxiv.org/abs/1706.01307.
|
|
|
|
|
* :math:`b`: Bias value, a 1-D tensor with shape [M].
|
|
|
|
|
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
|
|
|
|
|
|
|
|
|
|
Args:
|
|
|
|
|
x (Tensor): The input is 4-D SparseCooTensor with shape [N, H, W, C], the data
|
|
|
|
|
type of input is float16 or float32 or float64.
|
|
|
|
|
weight (Tensor): The convolution kernel, a Tensor with shape [kH, kW, C/g, M],
|
|
|
|
|
where M is the number of filters(output channels), g is the number of groups,
|
|
|
|
|
kD, kH, kW are the filter's height and width respectively.
|
2024-07-10 13:51:34 +08:00
|
|
|
bias (Tensor|None, optional): The bias, a Tensor of shape [M].
|
2024-04-22 13:33:02 +08:00
|
|
|
stride (int|list|tuple, optional): The stride size. It means the stride in convolution. If stride is a
|
|
|
|
|
list/tuple, it must contain two integers, (stride_height, stride_width).
|
|
|
|
|
Otherwise, stride_height = stride_width = stride. Default: stride = 1.
|
|
|
|
|
padding (string|int|list|tuple, optional): The padding size. It means the number of zero-paddings
|
|
|
|
|
on both sides for each dimension. If `padding` is a string, either 'VALID' or
|
|
|
|
|
'SAME' which is the padding algorithm. If padding size is a tuple or list,
|
|
|
|
|
it could be in three forms: `[pad_height, pad_width]` or
|
|
|
|
|
`[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
|
|
|
|
|
when `data_format` is `"NHWC"`, `padding` can be in the form
|
|
|
|
|
`[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
|
|
|
|
|
Default: padding = 0.
|
|
|
|
|
dilation (int|list|tuple, optional): The dilation size. It means the spacing between the kernel points.
|
|
|
|
|
If dilation is a list/tuple, it must contain two integers, (dilation_height,
|
|
|
|
|
dilation_width). Otherwise, dilation_height = dilation_width = dilation.
|
|
|
|
|
Default: dilation = 1.
|
|
|
|
|
groups (int, optional): The groups number of the Conv2D Layer. According to grouped
|
|
|
|
|
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
|
|
|
|
|
the first half of the filters is only connected to the first half
|
|
|
|
|
of the input channels, while the second half of the filters is only
|
|
|
|
|
connected to the second half of the input channels. Default: groups=1. Currently, only support groups=1.
|
|
|
|
|
data_format (str, optional): Specify the data format of the input, and the data format of the output
|
|
|
|
|
will be consistent with that of the input. An optional string from: `"NHWC"`.
|
|
|
|
|
The default is `"NHWC"`. When it is `"NHWC"`, the data is stored in the order of:
|
|
|
|
|
`[batch_size, input_height, input_width, input_channels]`.
|
2024-07-10 13:51:34 +08:00
|
|
|
key(str|None, optional): the key is used to save or use the same rulebook,
|
2024-04-22 13:33:02 +08:00
|
|
|
the definition and role of rulebook refers to
|
|
|
|
|
https://pdfs.semanticscholar.org/5125/a16039cabc6320c908a4764f32596e018ad3.pdf. The
|
|
|
|
|
default value is None.
|
2024-07-10 13:51:34 +08:00
|
|
|
name(str|None, optional): For detailed information, please refer
|
2024-04-22 13:33:02 +08:00
|
|
|
to :ref:`api_guide_Name`. Usually name is no need to set and
|
|
|
|
|
None by default.
|
|
|
|
|
|
|
|
|
|
Returns:
|
|
|
|
|
A SparseCooTensor representing the conv2d, whose data type is the same with input.
|
|
|
|
|
|
|
|
|
|
Examples:
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
|
|
|
.. code-block:: pycon
|
2024-04-22 13:33:02 +08:00
|
|
|
|
|
|
|
|
>>> import paddle
|
|
|
|
|
|
|
|
|
|
>>> indices = [[0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
|
|
|
|
|
>>> values = [[1], [2], [3], [4]]
|
|
|
|
|
>>> indices = paddle.to_tensor(indices, dtype='int32')
|
|
|
|
|
>>> values = paddle.to_tensor(values, dtype='float32')
|
|
|
|
|
>>> dense_shape = [1, 3, 4, 1]
|
|
|
|
|
>>> sparse_x = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape, stop_gradient=True)
|
|
|
|
|
>>> weight = paddle.randn((3, 3, 1, 1), dtype='float32')
|
|
|
|
|
>>> y = paddle.sparse.nn.functional.subm_conv2d(sparse_x, weight)
|
|
|
|
|
>>> print(y.shape)
|
[CodeStyle][Xdoctest][8,12,15,16,18-20,22-26,29-32,34-36,38-40,42,43,45-48,50,51,60-65,75,80,82,83,85-87,89-94,99-141,143,145,147-167,169-187,207-220,257,258,260-275,277-313,315-325][API Compatibility] Update shape output format in documentation examples (#76574)
---------
Co-authored-by: SigureMo <sigure.qaq@gmail.com>
2025-11-26 10:31:46 +08:00
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paddle.Size([1, 3, 4, 1])
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2024-04-22 13:33:02 +08:00
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"""
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return _conv2d_igemm(
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x,
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weight,
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bias,
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stride,
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padding,
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dilation,
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groups,
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True,
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key,
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data_format,
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name,
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)
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