wehub-resource-sync
335 lines
9.2 KiB
Python
335 lines
9.2 KiB
Python
# Copyright (c) 2020 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 (
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TYPE_CHECKING,
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TypedDict,
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)
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from typing_extensions import NotRequired, Unpack
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import paddle
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from paddle import nn
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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 paddle import Tensor
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from paddle._typing import Size2
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class _MobileNetV1Options(TypedDict):
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num_classes: NotRequired[int]
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with_pool: NotRequired[bool]
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__all__ = []
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model_urls = {
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'mobilenetv1_1.0': (
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'https://paddle-hapi.bj.bcebos.com/models/mobilenetv1_1.0.pdparams',
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'3033ab1975b1670bef51545feb65fc45',
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)
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}
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class DepthwiseSeparable(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_channels1: int,
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out_channels2: int,
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num_groups: int,
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stride: Size2,
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scale: float,
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) -> None:
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super().__init__()
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self._depthwise_conv = ConvNormActivation(
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in_channels,
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int(out_channels1 * scale),
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kernel_size=3,
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stride=stride,
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padding=1,
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groups=int(num_groups * scale),
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)
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self._pointwise_conv = ConvNormActivation(
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int(out_channels1 * scale),
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int(out_channels2 * scale),
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kernel_size=1,
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stride=1,
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padding=0,
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)
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def forward(self, x: Tensor) -> Tensor:
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x = self._depthwise_conv(x)
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x = self._pointwise_conv(x)
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return x
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class MobileNetV1(nn.Layer):
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"""MobileNetV1 model from
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`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_.
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Args:
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scale (float, optional): Scale of channels in each layer. Default: 1.0.
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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 MobileNetV1 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 MobileNetV1
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>>> model = MobileNetV1()
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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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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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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.dwsl = []
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self.num_classes = num_classes
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self.with_pool = with_pool
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self.conv1 = ConvNormActivation(
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in_channels=3,
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out_channels=int(32 * scale),
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kernel_size=3,
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stride=2,
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padding=1,
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)
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dws21 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(32 * scale),
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out_channels1=32,
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out_channels2=64,
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num_groups=32,
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stride=1,
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scale=scale,
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),
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name="conv2_1",
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)
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self.dwsl.append(dws21)
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dws22 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(64 * scale),
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out_channels1=64,
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out_channels2=128,
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num_groups=64,
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stride=2,
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scale=scale,
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),
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name="conv2_2",
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)
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self.dwsl.append(dws22)
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dws31 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(128 * scale),
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out_channels1=128,
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out_channels2=128,
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num_groups=128,
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stride=1,
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scale=scale,
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),
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name="conv3_1",
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)
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self.dwsl.append(dws31)
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dws32 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(128 * scale),
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out_channels1=128,
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out_channels2=256,
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num_groups=128,
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stride=2,
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scale=scale,
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),
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name="conv3_2",
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)
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self.dwsl.append(dws32)
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dws41 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(256 * scale),
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out_channels1=256,
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out_channels2=256,
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num_groups=256,
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stride=1,
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scale=scale,
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),
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name="conv4_1",
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)
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self.dwsl.append(dws41)
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dws42 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(256 * scale),
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out_channels1=256,
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out_channels2=512,
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num_groups=256,
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stride=2,
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scale=scale,
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),
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name="conv4_2",
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)
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self.dwsl.append(dws42)
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for i in range(5):
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tmp = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(512 * scale),
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out_channels1=512,
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out_channels2=512,
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num_groups=512,
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stride=1,
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scale=scale,
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),
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name="conv5_" + str(i + 1),
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)
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self.dwsl.append(tmp)
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dws56 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(512 * scale),
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out_channels1=512,
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out_channels2=1024,
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num_groups=512,
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stride=2,
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scale=scale,
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),
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name="conv5_6",
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)
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self.dwsl.append(dws56)
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dws6 = self.add_sublayer(
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sublayer=DepthwiseSeparable(
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in_channels=int(1024 * scale),
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out_channels1=1024,
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out_channels2=1024,
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num_groups=1024,
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stride=1,
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scale=scale,
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),
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name="conv6",
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)
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self.dwsl.append(dws6)
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if with_pool:
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self.pool2d_avg = nn.AdaptiveAvgPool2D(1)
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if num_classes > 0:
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self.fc = nn.Linear(int(1024 * scale), num_classes)
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def forward(self, x: Tensor) -> Tensor:
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x = self.conv1(x)
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for dws in self.dwsl:
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x = dws(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, 1)
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x = self.fc(x)
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return x
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def _mobilenet(
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arch: str, pretrained: bool = False, **kwargs: Unpack[_MobileNetV1Options]
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) -> MobileNetV1:
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model = MobileNetV1(**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.load_dict(param)
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return model
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def mobilenet_v1(
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pretrained: bool = False,
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scale: float = 1.0,
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**kwargs: Unpack[_MobileNetV1Options],
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) -> MobileNetV1:
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"""MobileNetV1 from
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`"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications" <https://arxiv.org/abs/1704.04861>`_.
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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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scale (float, optional): Scale of channels in each layer. Default: 1.0.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`MobileNetV1 <api_paddle_vision_models_MobileNetV1>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of MobileNetV1 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 mobilenet_v1
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>>> # Build model
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>>> model = mobilenet_v1()
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>>> # Build model and load imagenet pretrained weight
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>>> # model = mobilenet_v1(pretrained=True)
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>>> # build mobilenet v1 with scale=0.5
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>>> model_scale = mobilenet_v1(scale=0.5)
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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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model = _mobilenet(
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'mobilenetv1_' + str(scale), pretrained, scale=scale, **kwargs
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)
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return model
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