add write a new model file #167
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zhanghuiyao
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Jul 7, 2023
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| __BASE__: | ||
| - '../coco.yaml' | ||
| - './hyp.scratch-high.yaml' | ||
| #可以通过__BASE__指定基础配置文件的路径，如上<br> | ||
| #其中coco.yaml为数据相关配置文件，hyp.scratch-high.yaml用于定义训练时的超参数 | ||
| #可参考[configs目录](../../../configs)下其他模型 |
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# __BASE__中的yaml表示用于继承的基础配置文件，重复的参数会被当前文件覆盖；
__BASE__:
- '../coco.yaml'
- './hyp.scratch-high.yaml'
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| backbone: #骨干网络部分的配置 | ||
| [[-1, 1, ConvNormAct, [32, 3, 1]], # 0 | ||
| [-1, 1, ConvNormAct, [64, 3, 2]], # 1-P1/2 | ||
| [-1, 1, Bottleneck, [64]], | ||
| [-1, 1, ConvNormAct, [128, 3, 2]], # 3-P2/4 | ||
| [-1, 2, Bottleneck, [128]], | ||
| [-1, 1, ConvNormAct, [256, 3, 2]], # 5-P3/8 | ||
| [-1, 8, Bottleneck, [256]], | ||
| ] | ||
| #第一个元素表示该层的 ID，-1 表示自动分配。 | ||
| #第二个元素表示该层的重复次数，1 表示只有一次，-1 表示不限次数。 | ||
| #第三个元素表示该层使用的操作类型，这里为 ConvNormAct 和 Bottleneck。其中，ConvNormAct 表示包含卷积、归一化和激活操作的基本卷积层，Bottleneck 表示包含瓶颈结构的卷积层。 | ||
| #第四个元素表示该层的超参数，包括输出通道数、卷积核尺寸、步长等。例如，[-1, 1, ConvNormAct, [32, 3, 1]] 表示一个输出通道数为 32、卷积核大小为 3x3、步长为 1 的 ConvNormAct 层。 |
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# 骨干网络部分的配置，每层的元素含义为
# [from, number, module, args]
# 以第一层为例，[-1, 1, ConvNormAct, [32, 3, 1]], 表示输入来自 `-1`(上一层) ，重复次数为 1，模块名为 ConvNormAct，模块输入参数为 [32, 3, 1]；
backbone:
[[-1, 1, ConvNormAct, [32, 3, 1]], # 0
[-1, 1, ConvNormAct, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, ConvNormAct, [128, 3, 2]], # 3-P2/4
[-1, 2, Bottleneck, [128]],
[-1, 1, ConvNormAct, [256, 3, 2]], # 5-P3/8
[-1, 8, Bottleneck, [256]],
]| @@ -1,2 +1,205 @@ | |||
| # 模型编写指南 | |||
| 即将到来 | |||
| 本文档提供MindYOLO编写自定义模型的教程。<br> | |||
| 支持使用YAML文件编写网络结构，也可以直接使用Python代码编写网络结构。 | |||
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我们可以 直接定义一个网络 也可以 使用yaml文件方式定义一个网络。
| from .mymodel import * | ||
| ``` | ||
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| ## 方法二 直接使用python代码来编写网络 |
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| model = create_model( | ||
| model_name="mymodel", | ||
| model_cfg=opt.net, | ||
| num_classes=opt.data.nc, | ||
| sync_bn=opt.sync_bn if hasattr(opt, "sync_bn") else False, | ||
| ) |
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如果要在create_model中使用文件名创建自定义的模型，那么需要先使用注册器进行注册
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| self.reset_parameter() | ||
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| def construct(self, x): | ||
| return self.model(x) | ||
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| def reset_parameter(self): #定义了reset_parameter方法用于初始化模型的参数。 | ||
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| initialize_defult(self) |
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- reset_parameter 定义可以删掉，这个不是必须的；
- 可以直接在 init 中直接调用 initialize_defult 并描述原因：
initialize_defult(self) # 可选，你可能需要initialize_defult方法以获得和pytorch一样的conv2d、dense层的初始化方式；
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| ## 在模型函数中创建Mymodel模型实例 | ||
| mymodel函数被@register_model装饰器修饰，使其成为可调用的模型。在函数内通过MYmodel类创建了一个模型对象，并返回该对象作为模型。 | ||
| ```python | ||
| @register_model | ||
| def mymodel(cfg, in_channels=3, num_classes=None, **kwargs) -> MYmodel: | ||
| """Get GoogLeNet model. | ||
| Refer to the base class `models.GoogLeNet` for more details.""" | ||
| model = MYmodel(cfg=cfg, in_channels=in_channels, num_classes=num_classes, **kwargs) | ||
| return model | ||
| ``` |
zhanghuiyao
reviewed
Jul 7, 2023
| model = MyModel() | ||
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| ``` | ||
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注册模型 (可选)
如果需要使用mindyolo接口初始化自定义的模型，那么需要先对模型进行 注册 和 导入
- 模型注册 # 注册后的模型可以通过 create_model 接口以模型名的方式进行访问；
... - 模型导入
...
zhanghuiyao
reviewed
Jul 11, 2023
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| ``` | ||
| 在construct方法中，定义了模型的前向传播过程。输入x经过卷积、激活函数、池化等操作后，通过展平操作将特征张量变为一维向量，然后通过全连接层得到最终的输出结果。 | ||
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| ```python |
| [-1, 8, Bottleneck, [256]], | ||
| ] | ||
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| head: #head部分的配置 |
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| initialize_defult() # 可选，你可能需要initialize_defult方法以获得和pytorch一样的conv2d、dense层的初始化方式； | ||
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| self.reset_parameter() |
write a new model file final edition write a new model file final2 edition write a new model file clean code
CaitinZhao
reviewed
Jul 11, 2023
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| ## 模型定义 | ||
| ### 1.直接使用python代码来编写网络 | ||
| ### 模块导入 |
CaitinZhao
reviewed
Jul 11, 2023
| ``` | ||
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| ### 创建模型 | ||
| 定义了一个继承自nn.Cell的模型类MyModel。在构造函数__init__中，定义模型的各个组件： |
CaitinZhao
reviewed
Jul 11, 2023
| x = self.fc(x) | ||
| return x | ||
| ``` | ||
| ### 创建模型实例 |
CaitinZhao
reviewed
Jul 11, 2023
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| ``` | ||
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| ## 2.使用yaml文件编写网络 |
CaitinZhao
reviewed
Jul 11, 2023
| - 新建对应的mymodel.py文件 | ||
| - 在mindyolo/models/_init_.py文件中引入该模型 | ||
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| ### 以下是编写mymodel.yaml文件的详细指导 |
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以下是编写mymodel.yaml文件的详细指导：
zhanghuiyao
approved these changes
Jul 14, 2023
CaitinZhao
approved these changes
Jul 17, 2023
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