wehub-resource-sync
304 lines
9.3 KiB
Python
304 lines
9.3 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 (
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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 (
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AdaptiveAvgPool2D,
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AvgPool2D,
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Conv2D,
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Dropout,
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Linear,
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MaxPool2D,
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)
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from paddle.nn.initializer import Uniform
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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 _GoogLeNetOptions(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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"googlenet": (
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams",
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"80c06f038e905c53ab32c40eca6e26ae",
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)
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}
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def xavier(channels: int, filter_size: int) -> ParamAttr:
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stdv = (3.0 / (filter_size**2 * channels)) ** 0.5
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param_attr = ParamAttr(initializer=Uniform(-stdv, stdv))
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return param_attr
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class ConvLayer(nn.Layer):
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def __init__(
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self,
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num_channels: int,
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num_filters: int,
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filter_size: int,
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stride: Size2 = 1,
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groups: int = 1,
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):
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super().__init__()
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self._conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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bias_attr=False,
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)
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def forward(self, inputs: Tensor) -> Tensor:
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y = self._conv(inputs)
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return y
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class Inception(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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filter1: int,
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filter3R: int,
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filter3: int,
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filter5R: int,
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filter5: int,
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proj: int,
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):
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super().__init__()
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self._conv1 = ConvLayer(input_channels, filter1, 1)
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self._conv3r = ConvLayer(input_channels, filter3R, 1)
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self._conv3 = ConvLayer(filter3R, filter3, 3)
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self._conv5r = ConvLayer(input_channels, filter5R, 1)
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self._conv5 = ConvLayer(filter5R, filter5, 5)
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self._pool = MaxPool2D(kernel_size=3, stride=1, padding=1)
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self._convprj = ConvLayer(input_channels, proj, 1)
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def forward(self, inputs: Tensor) -> Tensor:
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conv1 = self._conv1(inputs)
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conv3r = self._conv3r(inputs)
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conv3 = self._conv3(conv3r)
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conv5r = self._conv5r(inputs)
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conv5 = self._conv5(conv5r)
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pool = self._pool(inputs)
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convprj = self._convprj(pool)
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cat = paddle.concat([conv1, conv3, conv5, convprj], axis=1)
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cat = F.relu(cat)
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return cat
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class GoogLeNet(nn.Layer):
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"""GoogLeNet (Inception v1) model architecture from
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`"Going Deeper with Convolutions" <https://arxiv.org/pdf/1409.4842.pdf>`_.
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Args:
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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 GoogLeNet (Inception v1) 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 GoogLeNet
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>>> # Build model
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>>> model = GoogLeNet()
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>>> x = paddle.rand([1, 3, 224, 224])
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>>> out, out1, out2 = model(x)
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>>> print(out.shape, out1.shape, out2.shape)
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paddle.Size([1, 1000]) paddle.Size([1, 1000]) paddle.Size([1, 1000])
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"""
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num_classes: int
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with_pool: bool
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def __init__(self, num_classes: int = 1000, with_pool: bool = True) -> None:
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super().__init__()
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self.num_classes = num_classes
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self.with_pool = with_pool
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self._conv = ConvLayer(3, 64, 7, 2)
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self._pool = MaxPool2D(kernel_size=3, stride=2)
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self._conv_1 = ConvLayer(64, 64, 1)
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self._conv_2 = ConvLayer(64, 192, 3)
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self._ince3a = Inception(192, 192, 64, 96, 128, 16, 32, 32)
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self._ince3b = Inception(256, 256, 128, 128, 192, 32, 96, 64)
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self._ince4a = Inception(480, 480, 192, 96, 208, 16, 48, 64)
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self._ince4b = Inception(512, 512, 160, 112, 224, 24, 64, 64)
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self._ince4c = Inception(512, 512, 128, 128, 256, 24, 64, 64)
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self._ince4d = Inception(512, 512, 112, 144, 288, 32, 64, 64)
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self._ince4e = Inception(528, 528, 256, 160, 320, 32, 128, 128)
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self._ince5a = Inception(832, 832, 256, 160, 320, 32, 128, 128)
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self._ince5b = Inception(832, 832, 384, 192, 384, 48, 128, 128)
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if with_pool:
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# out
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self._pool_5 = AdaptiveAvgPool2D(1)
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# out1
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self._pool_o1 = AvgPool2D(kernel_size=5, stride=3)
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# out2
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self._pool_o2 = AvgPool2D(kernel_size=5, stride=3)
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if num_classes > 0:
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# out
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self._drop = Dropout(p=0.4, mode="downscale_in_infer")
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self._fc_out = Linear(
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1024, num_classes, weight_attr=xavier(1024, 1)
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)
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# out1
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self._conv_o1 = ConvLayer(512, 128, 1)
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self._fc_o1 = Linear(1152, 1024, weight_attr=xavier(2048, 1))
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self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer")
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self._out1 = Linear(1024, num_classes, weight_attr=xavier(1024, 1))
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# out2
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self._conv_o2 = ConvLayer(528, 128, 1)
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self._fc_o2 = Linear(1152, 1024, weight_attr=xavier(2048, 1))
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self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer")
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self._out2 = Linear(1024, num_classes, weight_attr=xavier(1024, 1))
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def forward(self, inputs: Tensor) -> tuple[Tensor, Tensor, Tensor]:
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x = self._conv(inputs)
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x = self._pool(x)
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x = self._conv_1(x)
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x = self._conv_2(x)
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x = self._pool(x)
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x = self._ince3a(x)
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x = self._ince3b(x)
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x = self._pool(x)
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ince4a = self._ince4a(x)
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x = self._ince4b(ince4a)
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x = self._ince4c(x)
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ince4d = self._ince4d(x)
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x = self._ince4e(ince4d)
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x = self._pool(x)
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x = self._ince5a(x)
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ince5b = self._ince5b(x)
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out, out1, out2 = ince5b, ince4a, ince4d
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if self.with_pool:
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out = self._pool_5(out)
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out1 = self._pool_o1(out1)
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out2 = self._pool_o2(out2)
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if self.num_classes > 0:
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out = self._drop(out)
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out = paddle.squeeze(out, axis=[2, 3])
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out = self._fc_out(out)
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out1 = self._conv_o1(out1)
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out1 = paddle.flatten(out1, start_axis=1, stop_axis=-1)
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out1 = self._fc_o1(out1)
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out1 = F.relu(out1)
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out1 = self._drop_o1(out1)
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out1 = self._out1(out1)
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out2 = self._conv_o2(out2)
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out2 = paddle.flatten(out2, start_axis=1, stop_axis=-1)
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out2 = self._fc_o2(out2)
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out2 = self._drop_o2(out2)
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out2 = self._out2(out2)
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return out, out1, out2
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def googlenet(
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pretrained: bool = False, **kwargs: Unpack[_GoogLeNetOptions]
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) -> GoogLeNet:
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"""GoogLeNet (Inception v1) model architecture from
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`"Going Deeper with Convolutions" <https://arxiv.org/pdf/1409.4842.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:`GoogLeNet <api_paddle_vision_models_GoogLeNet>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) 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 googlenet
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>>> # Build model
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>>> model = googlenet()
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>>> # Build model and load imagenet pretrained weight
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>>> # model = googlenet(pretrained=True)
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>>> x = paddle.rand([1, 3, 224, 224])
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>>> out, out1, out2 = model(x)
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>>> print(out.shape, out1.shape, out2.shape)
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paddle.Size([1, 1000]) paddle.Size([1, 1000]) paddle.Size([1, 1000])
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"""
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model = GoogLeNet(**kwargs)
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arch = "googlenet"
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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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