# ***************************************************************
# Copyright (c) 2023 Jittor. All Rights Reserved.
# Maintainers:
# Wenyang Zhou <576825820@qq.com>
# Dun Liang <randonlang@gmail.com>.
#
# This file is subject to the terms and conditions defined in
# file 'LICENSE.txt', which is part of this source code package.
# ***************************************************************
# This model is generated by pytorch converter.
import jittor as jt
from jittor import nn
__all__ = ['GoogLeNet', 'googlenet']
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def googlenet(pretrained=False, **kwargs):
'''
构建一个GoogLeNet模型
GoogLeNet源自论文 `Going Deeper with Convolutions <https://arxiv.org/abs/1409.4842>`__ , 它由多个称为Inception模块的子网络构成。
参数:
- `pretrained` (bool, optional): 表示是否预加载预训练模型。默认为 `False`。
- `kwargs`: 其他optional参数。
返回值:
- 返回构建好的googlenet模型实例。如果 `pretrained` 为 `True`, 则返回在ImageNet上预训练的模型。
代码示例:
>>> import jittor as jt
>>> from jittor.models.googlenet import *
>>> net = googlenet(pretrained=False)
>>> x = jt.rand(1, 3, 224, 224)
>>> y = net(x)
>>> y.shape
[1, 1000]
'''
model = GoogLeNet(**kwargs)
if pretrained: model.load("jittorhub://googlenet.pkl")
return model
[文档]
class GoogLeNet(nn.Module):
'''
GoogLeNet源自论文 `Going Deeper with Convolutions <https://arxiv.org/abs/1409.4842>`__ , 也称为Inception v1, 它由多个称为Inception模块的子网络构成。
注意:
- 输入数据的维度应当是[batch_size, 3, height, width], 且height和width应该足够大, 以保证在网络结构中可以经历多次池化操作后依然保有空间分辨率。
- 如果关闭了aux_logits, aux1和aux2的输出将为None。
参数:
- num_classes (int, optional): 分类的类别数量, 默认值: 1000。
- aux_logits (bool, optional): 若为True, 则添加辅助分支以帮助训练。默认值: 1000。
- init_weights (bool, optional): 若为True, 则对模型的权重进行初始化。默认值: True。
- blocks (list[nn.Module]): 由三个模块组成的列表, 依次为[conv_block, inception_block, inception_aux_block]。若为None, 则使用[BasicConv2d, Inception, InceptionAux]作为默认值。默认值: None。
属性:
- conv1 (nn.Module): 输入层的卷积层。
- maxpool1 (nn.Pool): 输入层的最大池化层。
- conv2 (nn.Module): 第二个卷积层。
- conv3 (nn.Module): 第三个卷积层。
- maxpool2 (nn.Pool): 第二个最大池化层。
- inception3a (nn.Module): 第一个Inception模块。
- inception3b (nn.Module): 第二个Inception模块。
- maxpool3 (nn.Pool): 第三个最大池化层。
- inception4a (nn.Module): 第三个Inception模块。
- inception4b (nn.Module): 第四个Inception模块。
- inception4c (nn.Module): 第五个Inception模块。
- inception4d (nn.Module): 第六个Inception模块。
- inception4e (nn.Module): 第七个Inception模块。
- maxpool4 (nn.Pool): 第四个最大池化层。
- inception5a (nn.Module): 第八个Inception模块。
- inception5b (nn.Module): 第九个Inception模块。
- aux1 (nn.Module): 第一个辅助分类器。
- aux2 (nn.Module): 第二个辅助分类器。
- avgpool (nn.AdaptiveAvgPool2d): 全局平均池化层。
- dropout (nn.Dropout): 丢弃层。
- fc (nn.Linear): 全连接层。
代码示例:
>>> import jittor as jt
>>> from jittor.models.googlenet import GoogLeNet
>>> model = GoogLeNet(num_classes=1000, aux_logits=True, init_weights=True)
>>> input_tensor = jt.randn(1, 3, 224, 224)
>>> model(input_tensor).shape
[1,1000,]
'''
def __init__(self, num_classes=1000, aux_logits=True, init_weights=True, blocks=None):
super(GoogLeNet, self).__init__()
if (blocks is None):
blocks = [BasicConv2d, Inception, InceptionAux]
assert (len(blocks) == 3)
conv_block = blocks[0]
inception_block = blocks[1]
inception_aux_block = blocks[2]
self.aux_logits = aux_logits
self.conv1 = conv_block(3, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.Pool(3, stride=2, ceil_mode=True, op='maximum')
self.conv2 = conv_block(64, 64, kernel_size=1)
self.conv3 = conv_block(64, 192, kernel_size=3, padding=1)
self.maxpool2 = nn.Pool(3, stride=2, ceil_mode=True, op='maximum')
self.inception3a = inception_block(192, 64, 96, 128, 16, 32, 32)
self.inception3b = inception_block(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.Pool(3, stride=2, ceil_mode=True, op='maximum')
self.inception4a = inception_block(480, 192, 96, 208, 16, 48, 64)
self.inception4b = inception_block(512, 160, 112, 224, 24, 64, 64)
self.inception4c = inception_block(512, 128, 128, 256, 24, 64, 64)
self.inception4d = inception_block(512, 112, 144, 288, 32, 64, 64)
self.inception4e = inception_block(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.Pool(2, stride=2, ceil_mode=True, op='maximum')
self.inception5a = inception_block(832, 256, 160, 320, 32, 128, 128)
self.inception5b = inception_block(832, 384, 192, 384, 48, 128, 128)
if aux_logits:
self.aux1 = inception_aux_block(512, num_classes)
self.aux2 = inception_aux_block(528, num_classes)
else:
self.aux1 = None
self.aux2 = None
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(0.2)
self.fc = nn.Linear(1024, num_classes)
def _forward(self, x):
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.maxpool2(x)
x = self.inception3a(x)
x = self.inception3b(x)
x = self.maxpool3(x)
x = self.inception4a(x)
if (self.aux1 is not None):
aux1 = self.aux1(x)
x = self.inception4b(x)
x = self.inception4c(x)
x = self.inception4d(x)
if (self.aux2 is not None):
aux2 = self.aux2(x)
x = self.inception4e(x)
x = self.maxpool4(x)
x = self.inception5a(x)
x = self.inception5b(x)
x = self.avgpool(x)
x = jt.reshape(x, (x.shape[0], (- 1)))
x = self.dropout(x)
x = self.fc(x)
return (x, aux2, aux1)
def eager_outputs(self, x, aux2, aux1):
return x
def execute(self, x):
(x, aux1, aux2) = self._forward(x)
aux_defined = (self.aux_logits)
return self.eager_outputs(x, aux2, aux1)
class Inception(nn.Module):
'''
Inception模块是一种结构化的多分支神经网络架构单元, 该架构在GoogleNet中被广泛使用。它允许模型在同一层上学习多种尺度的特征。
Inception模块包含四个分支:
- 1x1的卷积
- 1x1的卷积接3x3的卷积
- 1x1的卷积接5x5的卷积
- 最大池化层接1x1的卷积
通过1x1卷积分支可以进行特征降维来减少计算复杂度。此模块的每个分支都可以被认为是执行不同类型的特征提取。分支之间的结果会按深度(通道数)连接在一起, 从而在单个卷积层中进行多尺度特征的融合。
参数:
- in_channels (int): 输入通道的数量。
- ch1x1 (int): 第一个分支的卷积层的输出通道数。
- ch3x3red (int): 为3x3卷积层之前的1x1卷积做减维所用的输出通道数。
- ch3x3 (int): 第二个分支中3x3卷积层后的输出通道数。
- ch5x5red (int): 为5x5卷积层之前的1x1卷积做减维所用的输出通道数。
- ch5x5 (int): 第三个分支中5x5卷积层后的输出通道数。
- pool_proj (int): 最大池化之后1x1卷积的输出通道数。
- conv_block (callable, optional): 用于构建卷积层的类或函数。 `None` 表示 `BasicConv2d`。默认值: `None`
代码示例:
>>> import jittor as jt
>>> from jittor.models.googlenet import Inception
>>> inc = Inception(in_channels=192, ch1x1=64, ch3x3red=96, ch3x3=128,
... ch5x5red=16, ch5x5=32, pool_proj=32)
>>> input = jt.randn(1, 192, 28, 28)
>>> inc(input).shape
[1,256,28,28,]
>>> # 四个分支分别生成 64, 128, 32, 32 通道并在深度上拼接
'''
def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj, conv_block=None):
super(Inception, self).__init__()
if (conv_block is None):
conv_block = BasicConv2d
self.branch1 = conv_block(in_channels, ch1x1, kernel_size=1)
self.branch2 = nn.Sequential(conv_block(in_channels, ch3x3red, kernel_size=1), conv_block(ch3x3red, ch3x3, kernel_size=3, padding=1))
self.branch3 = nn.Sequential(conv_block(in_channels, ch5x5red, kernel_size=1), conv_block(ch5x5red, ch5x5, kernel_size=3, padding=1))
self.branch4 = nn.Sequential(nn.Pool(kernel_size=3, stride=1, padding=1, ceil_mode=True, op='maximum'), conv_block(in_channels, pool_proj, kernel_size=1))
def _forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
outputs = [branch1, branch2, branch3, branch4]
return outputs
def execute(self, x):
outputs = self._forward(x)
return jt.concat(outputs, dim=1)
class InceptionAux(nn.Module):
def __init__(self, in_channels, num_classes, conv_block=None):
super(InceptionAux, self).__init__()
if (conv_block is None):
conv_block = BasicConv2d
self.conv = conv_block(in_channels, 128, kernel_size=1)
self.fc1 = nn.Linear(2048, 1024)
self.fc2 = nn.Linear(1024, num_classes)
def execute(self, x):
x = nn.AdaptiveAvgPool2d(4)(x)
x = self.conv(x)
x = jt.reshape(x, (x.shape[0], (- 1)))
x = nn.relu(self.fc1(x))
x = nn.Dropout(0.7)(x)
x = self.fc2(x)
return x
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm(out_channels, eps=0.001)
def execute(self, x):
x = self.conv(x)
x = self.bn(x)
return nn.relu(x)