wehub-resource-sync
650 lines
21 KiB
Python
650 lines
21 KiB
Python
# Copyright (c) 2021 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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from typing import TYPE_CHECKING
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import paddle
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from paddle import nn
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from paddle.nn import AdaptiveAvgPool2D, Linear, MaxPool2D
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from paddle.utils.download import get_weights_path_from_url
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from ..ops import ConvNormActivation
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if TYPE_CHECKING:
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from collections.abc import Callable
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from typing import Literal, TypedDict
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from typing_extensions import NotRequired, Unpack
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from paddle import Tensor
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from paddle._typing import Size2
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_ShuffleNetArch = Literal[
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'shufflenet_v2_x0_25',
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'shufflenet_v2_x0_33',
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'shufflenet_v2_x0_5',
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'shufflenet_v2_x1_0',
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'shufflenet_v2_x1_5',
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'shufflenet_v2_x2_0',
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'shufflenet_v2_swish',
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]
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_ActivationType = Literal['relu', 'swish']
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class _ShuffleNetOptions(TypedDict):
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act: NotRequired[_ActivationType | None]
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with_pool: NotRequired[bool]
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num_classes: NotRequired[int]
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class _ShuffleNetSwishOptions(TypedDict):
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with_pool: NotRequired[bool]
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num_classes: NotRequired[int]
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__all__ = []
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model_urls: dict[str, tuple[str, str]] = {
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"shufflenet_v2_x0_25": (
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"https://paddle-hapi.bj.bcebos.com/models/shufflenet_v2_x0_25.pdparams",
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"1e509b4c140eeb096bb16e214796d03b",
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),
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"shufflenet_v2_x0_33": (
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"https://paddle-hapi.bj.bcebos.com/models/shufflenet_v2_x0_33.pdparams",
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"3d7b3ab0eaa5c0927ff1026d31b729bd",
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),
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"shufflenet_v2_x0_5": (
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"https://paddle-hapi.bj.bcebos.com/models/shufflenet_v2_x0_5.pdparams",
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"5e5cee182a7793c4e4c73949b1a71bd4",
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),
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"shufflenet_v2_x1_0": (
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"https://paddle-hapi.bj.bcebos.com/models/shufflenet_v2_x1_0.pdparams",
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"122d42478b9e81eb49f8a9ede327b1a4",
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),
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"shufflenet_v2_x1_5": (
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"https://paddle-hapi.bj.bcebos.com/models/shufflenet_v2_x1_5.pdparams",
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"faced5827380d73531d0ee027c67826d",
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),
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"shufflenet_v2_x2_0": (
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"https://paddle-hapi.bj.bcebos.com/models/shufflenet_v2_x2_0.pdparams",
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"cd3dddcd8305e7bcd8ad14d1c69a5784",
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),
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"shufflenet_v2_swish": (
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"https://paddle-hapi.bj.bcebos.com/models/shufflenet_v2_swish.pdparams",
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"adde0aa3b023e5b0c94a68be1c394b84",
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),
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}
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def create_activation_layer(act: _ActivationType | None) -> nn.Layer | None:
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if act == "swish":
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return nn.Swish
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elif act == "relu":
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return nn.ReLU
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elif act is None:
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return None
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else:
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raise RuntimeError(f"The activation function is not supported: {act}")
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def channel_shuffle(x: Tensor, groups: int) -> Tensor:
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batch_size, num_channels, height, width = x.shape[0:4]
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channels_per_group = num_channels // groups
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# reshape
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x = paddle.reshape(
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x, shape=[batch_size, groups, channels_per_group, height, width]
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)
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# transpose
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x = paddle.transpose(x, perm=[0, 2, 1, 3, 4])
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# flatten
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x = paddle.reshape(x, shape=[batch_size, num_channels, height, width])
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return x
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class InvertedResidual(nn.Layer):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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stride: Size2,
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activation_layer: Callable[..., nn.Layer] = nn.ReLU,
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) -> None:
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super().__init__()
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self._conv_pw = ConvNormActivation(
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in_channels=in_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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activation_layer=activation_layer,
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)
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self._conv_dw = ConvNormActivation(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=out_channels // 2,
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activation_layer=None,
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)
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self._conv_linear = ConvNormActivation(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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activation_layer=activation_layer,
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)
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def forward(self, inputs: Tensor) -> Tensor:
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x1, x2 = paddle.split(
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inputs,
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num_or_sections=[inputs.shape[1] // 2, inputs.shape[1] // 2],
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axis=1,
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)
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x2 = self._conv_pw(x2)
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x2 = self._conv_dw(x2)
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x2 = self._conv_linear(x2)
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out = paddle.concat([x1, x2], axis=1)
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return channel_shuffle(out, 2)
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class InvertedResidualDS(nn.Layer):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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stride: Size2,
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activation_layer: Callable[..., nn.Layer] = nn.ReLU,
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) -> None:
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super().__init__()
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# branch1
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self._conv_dw_1 = ConvNormActivation(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=in_channels,
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activation_layer=None,
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)
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self._conv_linear_1 = ConvNormActivation(
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in_channels=in_channels,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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activation_layer=activation_layer,
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)
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# branch2
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self._conv_pw_2 = ConvNormActivation(
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in_channels=in_channels,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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activation_layer=activation_layer,
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)
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self._conv_dw_2 = ConvNormActivation(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=out_channels // 2,
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activation_layer=None,
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)
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self._conv_linear_2 = ConvNormActivation(
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in_channels=out_channels // 2,
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out_channels=out_channels // 2,
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kernel_size=1,
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stride=1,
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padding=0,
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groups=1,
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activation_layer=activation_layer,
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)
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def forward(self, inputs: Tensor) -> Tensor:
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x1 = self._conv_dw_1(inputs)
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x1 = self._conv_linear_1(x1)
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x2 = self._conv_pw_2(inputs)
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x2 = self._conv_dw_2(x2)
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x2 = self._conv_linear_2(x2)
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out = paddle.concat([x1, x2], axis=1)
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return channel_shuffle(out, 2)
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class ShuffleNetV2(nn.Layer):
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"""ShuffleNetV2 model from
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`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
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Args:
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scale (float, optional): Scale of output channels. Default: True.
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act (str, optional): Activation function of neural network. Default: "relu".
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num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
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will not be defined. Default: 1000.
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with_pool (bool, optional): Use pool before the last fc layer or not. Default: True.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ShuffleNetV2 model.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.vision.models import ShuffleNetV2
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>>> shufflenet_v2_swish = ShuffleNetV2(scale=1.0, act="swish")
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>>> x = paddle.rand([1, 3, 224, 224])
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>>> out = shufflenet_v2_swish(x)
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>>> print(out.shape)
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paddle.Size([1, 1000])
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"""
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scale: float
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num_classes: int
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with_pool: bool
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def __init__(
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self,
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scale: float = 1.0,
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act: _ActivationType | None = "relu",
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num_classes: int = 1000,
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with_pool: bool = True,
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) -> None:
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super().__init__()
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self.scale = scale
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self.num_classes = num_classes
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self.with_pool = with_pool
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stage_repeats = [4, 8, 4]
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activation_layer = create_activation_layer(act)
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if scale == 0.25:
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stage_out_channels = [-1, 24, 24, 48, 96, 512]
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elif scale == 0.33:
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stage_out_channels = [-1, 24, 32, 64, 128, 512]
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elif scale == 0.5:
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stage_out_channels = [-1, 24, 48, 96, 192, 1024]
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elif scale == 1.0:
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stage_out_channels = [-1, 24, 116, 232, 464, 1024]
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elif scale == 1.5:
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stage_out_channels = [-1, 24, 176, 352, 704, 1024]
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elif scale == 2.0:
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stage_out_channels = [-1, 24, 224, 488, 976, 2048]
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else:
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raise NotImplementedError(
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"This scale size:[" + str(scale) + "] is not implemented!"
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)
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# 1. conv1
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self._conv1 = ConvNormActivation(
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in_channels=3,
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out_channels=stage_out_channels[1],
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kernel_size=3,
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stride=2,
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padding=1,
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activation_layer=activation_layer,
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)
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self._max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
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# 2. bottleneck sequences
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self._block_list = []
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for stage_id, num_repeat in enumerate(stage_repeats):
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for i in range(num_repeat):
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if i == 0:
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block = self.add_sublayer(
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sublayer=InvertedResidualDS(
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in_channels=stage_out_channels[stage_id + 1],
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out_channels=stage_out_channels[stage_id + 2],
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stride=2,
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activation_layer=activation_layer,
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),
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name=str(stage_id + 2) + "_" + str(i + 1),
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)
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else:
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block = self.add_sublayer(
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sublayer=InvertedResidual(
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in_channels=stage_out_channels[stage_id + 2],
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out_channels=stage_out_channels[stage_id + 2],
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stride=1,
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activation_layer=activation_layer,
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),
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name=str(stage_id + 2) + "_" + str(i + 1),
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)
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self._block_list.append(block)
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# 3. last_conv
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self._last_conv = ConvNormActivation(
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in_channels=stage_out_channels[-2],
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out_channels=stage_out_channels[-1],
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kernel_size=1,
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stride=1,
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padding=0,
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activation_layer=activation_layer,
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)
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# 4. pool
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if with_pool:
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self._pool2d_avg = AdaptiveAvgPool2D(1)
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# 5. fc
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if num_classes > 0:
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self._out_c = stage_out_channels[-1]
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self._fc = Linear(stage_out_channels[-1], num_classes)
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def forward(self, inputs: Tensor) -> Tensor:
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x = self._conv1(inputs)
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x = self._max_pool(x)
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for inv in self._block_list:
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x = inv(x)
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x = self._last_conv(x)
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if self.with_pool:
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x = self._pool2d_avg(x)
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if self.num_classes > 0:
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x = paddle.flatten(x, start_axis=1, stop_axis=-1)
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x = self._fc(x)
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return x
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def _shufflenet_v2(
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arch: _ShuffleNetArch,
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pretrained: bool = False,
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scale: float = 1.0,
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**kwargs: Unpack[_ShuffleNetOptions],
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) -> ShuffleNetV2:
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model = ShuffleNetV2(scale=scale, **kwargs)
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if pretrained:
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assert arch in model_urls, (
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f"{arch} model do not have a pretrained model now, you should set pretrained=False"
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)
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weight_path = get_weights_path_from_url(
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model_urls[arch][0], model_urls[arch][1]
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)
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param = paddle.load(weight_path)
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model.set_dict(param)
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return model
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def shufflenet_v2_x0_25(
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pretrained: bool = False, **kwargs: Unpack[_ShuffleNetOptions]
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) -> ShuffleNetV2:
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"""ShuffleNetV2 with 0.25x output channels, as described in
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`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ShuffleNetV2 <api_paddle_vision_models_ShuffleNetV2>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ShuffleNetV2 with 0.25x output channels.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.vision.models import shufflenet_v2_x0_25
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>>> # build model
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>>> model = shufflenet_v2_x0_25()
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>>> # build model and load imagenet pretrained weight
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>>> # model = shufflenet_v2_x0_25(pretrained=True)
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>>> x = paddle.rand([1, 3, 224, 224])
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>>> out = model(x)
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>>> print(out.shape)
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paddle.Size([1, 1000])
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"""
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return _shufflenet_v2(
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"shufflenet_v2_x0_25", scale=0.25, pretrained=pretrained, **kwargs
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)
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def shufflenet_v2_x0_33(
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pretrained: bool = False, **kwargs: Unpack[_ShuffleNetOptions]
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) -> ShuffleNetV2:
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"""ShuffleNetV2 with 0.33x output channels, as described in
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`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ShuffleNetV2 <api_paddle_vision_models_ShuffleNetV2>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ShuffleNetV2 with 0.33x output channels.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.vision.models import shufflenet_v2_x0_33
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>>> # build model
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>>> model = shufflenet_v2_x0_33()
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>>> # build model and load imagenet pretrained weight
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>>> # model = shufflenet_v2_x0_33(pretrained=True)
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>>> x = paddle.rand([1, 3, 224, 224])
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>>> out = model(x)
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>>> print(out.shape)
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paddle.Size([1, 1000])
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"""
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return _shufflenet_v2(
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"shufflenet_v2_x0_33", scale=0.33, pretrained=pretrained, **kwargs
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)
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def shufflenet_v2_x0_5(
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pretrained: bool = False, **kwargs: Unpack[_ShuffleNetOptions]
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) -> ShuffleNetV2:
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"""ShuffleNetV2 with 0.5x output channels, as described in
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`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ShuffleNetV2 <api_paddle_vision_models_ShuffleNetV2>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of ShuffleNetV2 with 0.5x output channels.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.vision.models import shufflenet_v2_x0_5
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>>> # build model
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>>> model = shufflenet_v2_x0_5()
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>>> # build model and load imagenet pretrained weight
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>>> # model = shufflenet_v2_x0_5(pretrained=True)
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>>> x = paddle.rand([1, 3, 224, 224])
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>>> out = model(x)
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>>> print(out.shape)
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paddle.Size([1, 1000])
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"""
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return _shufflenet_v2(
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"shufflenet_v2_x0_5", scale=0.5, pretrained=pretrained, **kwargs
|
|
)
|
|
|
|
|
|
def shufflenet_v2_x1_0(
|
|
pretrained: bool = False, **kwargs: Unpack[_ShuffleNetOptions]
|
|
) -> ShuffleNetV2:
|
|
"""ShuffleNetV2 with 1.0x output channels, as described in
|
|
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ShuffleNetV2 <api_paddle_vision_models_ShuffleNetV2>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ShuffleNetV2 with 1.0x output channels.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
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|
>>> from paddle.vision.models import shufflenet_v2_x1_0
|
|
|
|
>>> # build model
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|
>>> model = shufflenet_v2_x1_0()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = shufflenet_v2_x1_0(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _shufflenet_v2(
|
|
"shufflenet_v2_x1_0", scale=1.0, pretrained=pretrained, **kwargs
|
|
)
|
|
|
|
|
|
def shufflenet_v2_x1_5(
|
|
pretrained: bool = False, **kwargs: Unpack[_ShuffleNetOptions]
|
|
) -> ShuffleNetV2:
|
|
"""ShuffleNetV2 with 1.5x output channels, as described in
|
|
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ShuffleNetV2 <api_paddle_vision_models_ShuffleNetV2>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ShuffleNetV2 with 1.5x output channels.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import shufflenet_v2_x1_5
|
|
|
|
>>> # build model
|
|
>>> model = shufflenet_v2_x1_5()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = shufflenet_v2_x1_5(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _shufflenet_v2(
|
|
"shufflenet_v2_x1_5", scale=1.5, pretrained=pretrained, **kwargs
|
|
)
|
|
|
|
|
|
def shufflenet_v2_x2_0(
|
|
pretrained: bool = False, **kwargs: Unpack[_ShuffleNetOptions]
|
|
) -> ShuffleNetV2:
|
|
"""ShuffleNetV2 with 2.0x output channels, as described in
|
|
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ShuffleNetV2 <api_paddle_vision_models_ShuffleNetV2>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ShuffleNetV2 with 2.0x output channels.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import shufflenet_v2_x2_0
|
|
|
|
>>> # build model
|
|
>>> model = shufflenet_v2_x2_0()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = shufflenet_v2_x2_0(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _shufflenet_v2(
|
|
"shufflenet_v2_x2_0", scale=2.0, pretrained=pretrained, **kwargs
|
|
)
|
|
|
|
|
|
def shufflenet_v2_swish(
|
|
pretrained: bool = False, **kwargs: Unpack[_ShuffleNetSwishOptions]
|
|
) -> ShuffleNetV2:
|
|
"""ShuffleNetV2 with swish activation function, as described in
|
|
`"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" <https://arxiv.org/pdf/1807.11164.pdf>`_.
|
|
|
|
Args:
|
|
pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
|
|
on ImageNet. Default: False.
|
|
**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`ShuffleNetV2 <api_paddle_vision_models_ShuffleNetV2>`.
|
|
|
|
Returns:
|
|
:ref:`api_paddle_nn_Layer`. An instance of ShuffleNetV2 with swish activation function.
|
|
|
|
Examples:
|
|
.. code-block:: pycon
|
|
|
|
>>> import paddle
|
|
>>> from paddle.vision.models import shufflenet_v2_swish
|
|
|
|
>>> # build model
|
|
>>> model = shufflenet_v2_swish()
|
|
|
|
>>> # build model and load imagenet pretrained weight
|
|
>>> # model = shufflenet_v2_swish(pretrained=True)
|
|
|
|
>>> x = paddle.rand([1, 3, 224, 224])
|
|
>>> out = model(x)
|
|
|
|
>>> print(out.shape)
|
|
paddle.Size([1, 1000])
|
|
"""
|
|
return _shufflenet_v2(
|
|
"shufflenet_v2_swish",
|
|
scale=1.0,
|
|
act="swish",
|
|
pretrained=pretrained,
|
|
**kwargs,
|
|
)
|