wehub-resource-sync
321 lines
10 KiB
Python
321 lines
10 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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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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import paddle.nn.functional as F
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from paddle import nn
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from paddle.base.param_attr import ParamAttr
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from paddle.nn import AdaptiveAvgPool2D, Conv2D, Dropout, MaxPool2D
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from paddle.utils.download import get_weights_path_from_url
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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 _SqueezeNetOptions(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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'squeezenet1_0': (
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'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams',
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'30b95af60a2178f03cf9b66cd77e1db1',
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),
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'squeezenet1_1': (
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'https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams',
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'a11250d3a1f91d7131fd095ebbf09eee',
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),
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}
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class MakeFireConv(nn.Layer):
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def __init__(
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self,
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input_channels: int,
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output_channels: int,
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filter_size: Size2,
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padding: Size2 = 0,
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) -> None:
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super().__init__()
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self._conv = Conv2D(
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input_channels,
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output_channels,
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filter_size,
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padding=padding,
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weight_attr=ParamAttr(),
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bias_attr=ParamAttr(),
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)
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def forward(self, x: Tensor) -> Tensor:
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x = self._conv(x)
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x = F.relu(x)
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return x
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class MakeFire(nn.Layer):
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def __init__(
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self,
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input_channels: int,
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squeeze_channels: int,
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expand1x1_channels: int,
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expand3x3_channels: int,
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) -> None:
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super().__init__()
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self._conv = MakeFireConv(input_channels, squeeze_channels, 1)
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self._conv_path1 = MakeFireConv(squeeze_channels, expand1x1_channels, 1)
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self._conv_path2 = MakeFireConv(
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squeeze_channels, expand3x3_channels, 3, padding=1
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)
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def forward(self, inputs: Tensor) -> Tensor:
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x = self._conv(inputs)
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x1 = self._conv_path1(x)
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x2 = self._conv_path2(x)
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return paddle.concat([x1, x2], axis=1)
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class SqueezeNet(nn.Layer):
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"""SqueezeNet model from
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`"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
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<https://arxiv.org/pdf/1602.07360.pdf>`_.
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Args:
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version (str): Version of SqueezeNet, which can be "1.0" or "1.1".
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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 SqueezeNet 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 SqueezeNet
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>>> # build v1.0 model
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>>> model = SqueezeNet(version='1.0')
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>>> # build v1.1 model
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>>> # model = SqueezeNet(version='1.1')
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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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version: str
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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, version: str, num_classes: int = 1000, with_pool: bool = True
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) -> None:
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super().__init__()
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self.version = version
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self.num_classes = num_classes
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self.with_pool = with_pool
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supported_versions = ['1.0', '1.1']
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assert version in supported_versions, (
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f"supported versions are {supported_versions} but input version is {version}"
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)
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if self.version == "1.0":
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self._conv = Conv2D(
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3,
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96,
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7,
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stride=2,
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weight_attr=ParamAttr(),
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bias_attr=ParamAttr(),
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)
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self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
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self._conv1 = MakeFire(96, 16, 64, 64)
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self._conv2 = MakeFire(128, 16, 64, 64)
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self._conv3 = MakeFire(128, 32, 128, 128)
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self._conv4 = MakeFire(256, 32, 128, 128)
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self._conv5 = MakeFire(256, 48, 192, 192)
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self._conv6 = MakeFire(384, 48, 192, 192)
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self._conv7 = MakeFire(384, 64, 256, 256)
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self._conv8 = MakeFire(512, 64, 256, 256)
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else:
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self._conv = Conv2D(
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3,
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64,
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3,
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stride=2,
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padding=1,
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weight_attr=ParamAttr(),
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bias_attr=ParamAttr(),
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)
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self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
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self._conv1 = MakeFire(64, 16, 64, 64)
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self._conv2 = MakeFire(128, 16, 64, 64)
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self._conv3 = MakeFire(128, 32, 128, 128)
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self._conv4 = MakeFire(256, 32, 128, 128)
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self._conv5 = MakeFire(256, 48, 192, 192)
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self._conv6 = MakeFire(384, 48, 192, 192)
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self._conv7 = MakeFire(384, 64, 256, 256)
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self._conv8 = MakeFire(512, 64, 256, 256)
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self._drop = Dropout(p=0.5, mode="downscale_in_infer")
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self._conv9 = Conv2D(
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512, num_classes, 1, weight_attr=ParamAttr(), bias_attr=ParamAttr()
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)
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self._avg_pool = AdaptiveAvgPool2D(1)
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def forward(self, inputs: Tensor) -> Tensor:
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x = self._conv(inputs)
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x = F.relu(x)
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x = self._pool(x)
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if self.version == "1.0":
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x = self._conv1(x)
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x = self._conv2(x)
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x = self._conv3(x)
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x = self._pool(x)
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x = self._conv4(x)
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x = self._conv5(x)
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x = self._conv6(x)
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x = self._conv7(x)
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x = self._pool(x)
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x = self._conv8(x)
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else:
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x = self._conv1(x)
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x = self._conv2(x)
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x = self._pool(x)
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x = self._conv3(x)
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x = self._conv4(x)
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x = self._pool(x)
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x = self._conv5(x)
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x = self._conv6(x)
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x = self._conv7(x)
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x = self._conv8(x)
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if self.num_classes > 0:
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x = self._drop(x)
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x = self._conv9(x)
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if self.with_pool:
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x = F.relu(x)
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x = self._avg_pool(x)
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x = paddle.squeeze(x, axis=[2, 3])
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return x
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def _squeezenet(
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arch: str,
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version: str,
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pretrained: bool,
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**kwargs: Unpack[_SqueezeNetOptions],
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) -> SqueezeNet:
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model = SqueezeNet(version, **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 squeezenet1_0(
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pretrained: bool = False, **kwargs: Unpack[_SqueezeNetOptions]
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) -> SqueezeNet:
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"""SqueezeNet v1.0 model from
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`"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
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<https://arxiv.org/pdf/1602.07360.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:`SqueezeNet <api_paddle_vision_models_SqueezeNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of SqueezeNet v1.0 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 squeezenet1_0
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>>> # build model
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>>> model = squeezenet1_0()
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>>> # build model and load imagenet pretrained weight
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>>> # model = squeezenet1_0(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 _squeezenet('squeezenet1_0', '1.0', pretrained, **kwargs)
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def squeezenet1_1(
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pretrained: bool = False, **kwargs: Unpack[_SqueezeNetOptions]
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) -> SqueezeNet:
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"""SqueezeNet v1.1 model from
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`"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
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<https://arxiv.org/pdf/1602.07360.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:`SqueezeNet <api_paddle_vision_models_SqueezeNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of SqueezeNet v1.1 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 squeezenet1_1
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>>> # build model
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>>> model = squeezenet1_1()
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>>> # build model and load imagenet pretrained weight
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>>> # model = squeezenet1_1(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 _squeezenet('squeezenet1_1', '1.1', pretrained, **kwargs)
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