mirror of https://github.com/open-mmlab/mmcv.git
571 lines
19 KiB
Markdown
571 lines
19 KiB
Markdown
## 卷积神经网络
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我们为卷积神经网络提供了一些构建模块，包括层构建、模块组件和权重初始化。
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### 网络层的构建
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在运行实验时，我们可能需要尝试同属一种类型但不同配置的层，但又不希望每次都修改代码。于是我们提供一些层构建方法，可以从字典构建层，字典可以在配置文件中配置，也可以通过命令行参数指定。
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#### 用法
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一个简单的例子：
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```python
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cfg = dict(type='Conv3d')
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layer = build_conv_layer(cfg, in_channels=3, out_channels=8, kernel_size=3)
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```
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- `build_conv_layer`: 支持的类型包括 Conv1d、Conv2d、Conv3d、Conv (Conv是Conv2d的别名）
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- `build_norm_layer`: 支持的类型包括 BN1d、BN2d、BN3d、BN (alias for BN2d)、SyncBN、GN、LN、IN1d、IN2d、IN3d、IN（IN是IN2d的别名）
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- `build_activation_layer`：支持的类型包括 ReLU、LeakyReLU、PReLU、RReLU、ReLU6、ELU、Sigmoid、Tanh、GELU
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- `build_upsample_layer`: 支持的类型包括 nearest、bilinear、deconv、pixel_shuffle
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- `build_padding_layer`: 支持的类型包括 zero、reflect、replicate
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#### 拓展
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我们还允许自定义层和算子来扩展构建方法。
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1. 编写和注册自己的模块：
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```python
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from mmcv.cnn import UPSAMPLE_LAYERS
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@UPSAMPLE_LAYERS.register_module()
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class MyUpsample:
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def __init__(self, scale_factor):
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pass
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def forward(self, x):
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pass
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```
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2. 在某处导入 `MyUpsample` （例如 `__init__.py` ）然后使用它：
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```python
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cfg = dict(type='MyUpsample', scale_factor=2)
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layer = build_upsample_layer(cfg)
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```
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### 模块组件
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我们还提供了常用的模块组件，以方便网络构建。
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卷积组件 `ConvModule` 由 convolution、normalization以及activation layers 组成，更多细节请参考 [ConvModule api](api.html#mmcv.cnn.ConvModule)。
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```python
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# conv + bn + relu
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conv = ConvModule(3, 8, 2, norm_cfg=dict(type='BN'))
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# conv + gn + relu
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conv = ConvModule(3, 8, 2, norm_cfg=dict(type='GN', num_groups=2))
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# conv + relu
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conv = ConvModule(3, 8, 2)
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# conv
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conv = ConvModule(3, 8, 2, act_cfg=None)
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# conv + leaky relu
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conv = ConvModule(3, 8, 3, padding=1, act_cfg=dict(type='LeakyReLU'))
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# bn + conv + relu
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conv = ConvModule(
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3, 8, 2, norm_cfg=dict(type='BN'), order=('norm', 'conv', 'act'))
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```
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### Weight initialization
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> 实现细节可以在 [mmcv/cnn/utils/weight_init.py](../../mmcv/cnn/utils/weight_init.py)中找到
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在训练过程中，适当的初始化策略有利于加快训练速度或者获得更高的性能。 在MMCV中，我们提供了一些常用的方法来初始化模块，比如 `nn.Conv2d` 模块。当然，我们也提供了一些高级API，可用于初始化包含一个或多个模块的模型。
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#### Initialization functions
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以函数的方式初始化 `nn.Module` ，例如 `nn.Conv2d` 、 `nn.Linear` 等。
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我们提供以下初始化方法，
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- constant_init
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使用给定常量值初始化模型参数
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```python
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>>> import torch.nn as nn
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>>> from mmcv.cnn import constant_init
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>>> conv1 = nn.Conv2d(3, 3, 1)
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>>> # constant_init(module, val, bias=0)
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>>> constant_init(conv1, 1, 0)
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>>> conv1.weight
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```
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- xavier_init
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按照 [Understanding the difficulty of training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010)](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf) 描述的方法初始化模型参数
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```python
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>>> import torch.nn as nn
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>>> from mmcv.cnn import xavier_init
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>>> conv1 = nn.Conv2d(3, 3, 1)
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>>> # xavier_init(module, gain=1, bias=0, distribution='normal')
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>>> xavier_init(conv1, distribution='normal')
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```
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- normal_init
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使用正态分布（高斯分布）初始化模型参数
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```python
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>>> import torch.nn as nn
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>>> from mmcv.cnn import normal_init
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>>> conv1 = nn.Conv2d(3, 3, 1)
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>>> # normal_init(module, mean=0, std=1, bias=0)
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>>> normal_init(conv1, std=0.01, bias=0)
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```
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- uniform_init
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使用均匀分布初始化模型参数
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```python
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>>> import torch.nn as nn
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>>> from mmcv.cnn import uniform_init
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>>> conv1 = nn.Conv2d(3, 3, 1)
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>>> # uniform_init(module, a=0, b=1, bias=0)
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>>> uniform_init(conv1, a=0, b=1)
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```
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- kaiming_init
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按照 [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015)](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf) 描述的方法来初始化模型参数。
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```python
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>>> import torch.nn as nn
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>>> from mmcv.cnn import kaiming_init
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>>> conv1 = nn.Conv2d(3, 3, 1)
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>>> # kaiming_init(module, a=0, mode='fan_out', nonlinearity='relu', bias=0, distribution='normal')
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>>> kaiming_init(conv1)
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```
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- caffe2_xavier_init
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caffe2中实现的 `xavier initialization`，对应于 PyTorch中的 `kaiming_uniform_`
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```python
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>>> import torch.nn as nn
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>>> from mmcv.cnn import caffe2_xavier_init
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>>> conv1 = nn.Conv2d(3, 3, 1)
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>>> # caffe2_xavier_init(module, bias=0)
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>>> caffe2_xavier_init(conv1)
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```
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- bias_init_with_prob
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根据给定的概率初始化 `conv/fc`, 这在 [Focal Loss for Dense Object Detection](https://arxiv.org/pdf/1708.02002.pdf) 提出。
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```python
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>>> from mmcv.cnn import bias_init_with_prob
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>>> # bias_init_with_prob is proposed in Focal Loss
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>>> bias = bias_init_with_prob(0.01)
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>>> bias
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-4.59511985013459
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```
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#### Initializers and configs
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在初始化方法的基础上，我们定义了相应的初始化类，并将它们注册到 `INITIALIZERS` 中，这样我们就可以使用 `config` 配置来初始化模型了。
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我们提供以下初始化类：
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- ConstantInit
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- XavierInit
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- NormalInit
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- UniformInit
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- KaimingInit
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- Caffe2XavierInit
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- PretrainedInit
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接下来详细介绍 `initialize` 的使用方法
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1. 通过关键字 `layer` 来初始化模型
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如果我们只定义了关键字 `layer` ，那么只初始化 `layer` 中包含的层。
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注意: 关键字 `layer` 支持的模块是带有 weights 和 bias 属性的 PyTorch 模块，所以不支持 `MultiheadAttention layer`
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- 定义关键字 `layer` 列表并使用相同相同配置初始化模块
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```python
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import torch.nn as nn
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from mmcv.cnn import initialize
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class FooNet(nn.Module):
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def __init__(self):
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super().__init__()
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self.feat = nn.Conv1d(3, 1, 3)
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self.reg = nn.Conv2d(3, 3, 3)
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self.cls = nn.Linear(1, 2)
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model = FooNet()
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init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d', 'Linear'], val=1)
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# 使用相同的配置初始化整个模块
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initialize(model, init_cfg)
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# model.feat.weight
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# Parameter containing:
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# tensor([[[1., 1., 1.],
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# [1., 1., 1.],
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# [1., 1., 1.]]], requires_grad=True)
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```
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- 定义关键字 `layer` 用于初始化不同配置的层
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```python
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import torch.nn as nn
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from mmcv.cnn.utils import initialize
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class FooNet(nn.Module):
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def __init__(self):
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super().__init__()
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self.feat = nn.Conv1d(3, 1, 3)
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self.reg = nn.Conv2d(3, 3, 3)
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self.cls = nn.Linear(1,2)
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model = FooNet()
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init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
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dict(type='Constant', layer='Conv2d', val=2),
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dict(type='Constant', layer='Linear', val=3)]
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# nn.Conv1d 使用 dict(type='Constant', val=1) 初始化
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# nn.Conv2d 使用 dict(type='Constant', val=2) 初始化
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# nn.Linear 使用 dict(type='Constant', val=3) 初始化
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initialize(model, init_cfg)
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# model.reg.weight
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# Parameter containing:
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# tensor([[[[2., 2., 2.],
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# [2., 2., 2.],
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# [2., 2., 2.]],
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# ...,
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# [[2., 2., 2.],
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# [2., 2., 2.],
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# [2., 2., 2.]]]], requires_grad=True)
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```
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2. 定义关键字`override`初始化模型
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- 当用属性名初始化某个特定部分时, 我们可以使用关键字 `override`, 关键字 `override` 对应的Value会替代init_cfg中相应的值
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```python
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import torch.nn as nn
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from mmcv.cnn import initialize
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class FooNet(nn.Module):
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def __init__(self):
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super().__init__()
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self.feat = nn.Conv1d(3, 1, 3)
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self.reg = nn.Conv2d(3, 3, 3)
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self.cls = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))
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# 如果我们想将模型的权重初始化为 1，将偏差初始化为 2
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# 但希望 `reg` 中的权重为 3，偏差为 4，则我们可以使用关键字override
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model = FooNet()
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init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'], val=1, bias=2,
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override=dict(type='Constant', name='reg', val=3, bias=4))
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# 使用 dict(type='Constant', val=1, bias=2)来初始化 self.feat and self.cls
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# 使用dict(type='Constant', val=3, bias=4)来初始化‘reg’模块。
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initialize(model, init_cfg)
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# model.reg.weight
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# Parameter containing:
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# tensor([[[[3., 3., 3.],
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# [3., 3., 3.],
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# [3., 3., 3.]],
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# ...,
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# [[3., 3., 3.],
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# [3., 3., 3.],
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# [3., 3., 3.]]]], requires_grad=True)
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```
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- 如果 init_cfg 中的关键字`layer`为None，则只初始化在关键字override中的子模块，并且省略override中的 type 和其他参数
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```python
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model = FooNet()
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init_cfg = dict(type='Constant', val=1, bias=2, override=dict(name='reg'))
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# self.feat 和 self.cls 使用pyTorch默认的初始化
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# 将使用 dict(type='Constant', val=1, bias=2) 初始化名为 'reg' 的模块
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initialize(model, init_cfg)
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# model.reg.weight
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# Parameter containing:
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# tensor([[[[1., 1., 1.],
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# [1., 1., 1.],
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# [1., 1., 1.]],
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# ...,
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# [[1., 1., 1.],
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# [1., 1., 1.],
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# [1., 1., 1.]]]], requires_grad=True)
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```
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- 如果我们没有定义关键字`layer`或`override` , 将不会初始化任何东西
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- 关键字`override`的无效用法
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```python
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# 没有重写任何子模块
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init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'],
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val=1, bias=2,
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override=dict(type='Constant', val=3, bias=4))
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# 没有指定type，即便有其他参数，也是无效的。
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init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'],
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val=1, bias=2,
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override=dict(name='reg', val=3, bias=4))
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```
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3. 用预训练模型初始化
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```python
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import torch.nn as nn
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import torchvision.models as models
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from mmcv.cnn import initialize
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# 使用预训练模型来初始化
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model = models.resnet50()
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# model.conv1.weight
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# Parameter containing:
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# tensor([[[[-6.7435e-03, -2.3531e-02, -9.0143e-03, ..., -2.1245e-03,
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# -1.8077e-03, 3.0338e-03],
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# [-1.2603e-02, -2.7831e-02, 2.3187e-02, ..., -1.5793e-02,
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# 1.1655e-02, 4.5889e-03],
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# [-3.7916e-02, 1.2014e-02, 1.3815e-02, ..., -4.2651e-03,
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# 1.7314e-02, -9.9998e-03],
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# ...,
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init_cfg = dict(type='Pretrained',
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checkpoint='torchvision://resnet50')
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initialize(model, init_cfg)
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# model.conv1.weight
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# Parameter containing:
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# tensor([[[[ 1.3335e-02, 1.4664e-02, -1.5351e-02, ..., -4.0896e-02,
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# -4.3034e-02, -7.0755e-02],
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# [ 4.1205e-03, 5.8477e-03, 1.4948e-02, ..., 2.2060e-03,
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# -2.0912e-02, -3.8517e-02],
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# [ 2.2331e-02, 2.3595e-02, 1.6120e-02, ..., 1.0281e-01,
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# 6.2641e-02, 5.1977e-02],
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# ...,
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# 使用关键字'prefix'用预训练模型的特定部分来初始化子模块权重
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model = models.resnet50()
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url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/'\
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'retinanet_r50_fpn_1x_coco/'\
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'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
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init_cfg = dict(type='Pretrained',
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checkpoint=url, prefix='backbone.')
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initialize(model, init_cfg)
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```
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4. 初始化继承自BaseModule、Sequential、ModuleList、ModuleDict的模型
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`BaseModule` 继承自 `torch.nn.Module`, 它们之间唯一的不同是 `BaseModule` 实现了 `init_weight`
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`Sequential` 继承自 `BaseModule` 和 `torch.nn.Sequential`
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`ModuleList` 继承自 `BaseModule` 和 `torch.nn.ModuleList`
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`ModuleDict` 继承自 `BaseModule` 和 `torch.nn.ModuleDict`
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```python
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import torch.nn as nn
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from mmcv.runner import BaseModule, Sequential, ModuleList, ModuleDict
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class FooConv1d(BaseModule):
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def __init__(self, init_cfg=None):
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super().__init__(init_cfg)
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self.conv1d = nn.Conv1d(4, 1, 4)
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def forward(self, x):
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return self.conv1d(x)
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class FooConv2d(BaseModule):
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def __init__(self, init_cfg=None):
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super().__init__(init_cfg)
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self.conv2d = nn.Conv2d(3, 1, 3)
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def forward(self, x):
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return self.conv2d(x)
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# BaseModule
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init_cfg = dict(type='Constant', layer='Conv1d', val=0., bias=1.)
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model = FooConv1d(init_cfg)
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model.init_weights()
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# model.conv1d.weight
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# Parameter containing:
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# tensor([[[0., 0., 0., 0.],
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# [0., 0., 0., 0.],
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# [0., 0., 0., 0.],
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# [0., 0., 0., 0.]]], requires_grad=True)
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# Sequential
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init_cfg1 = dict(type='Constant', layer='Conv1d', val=0., bias=1.)
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init_cfg2 = dict(type='Constant', layer='Conv2d', val=2., bias=3.)
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model1 = FooConv1d(init_cfg1)
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model2 = FooConv2d(init_cfg2)
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seq_model = Sequential(model1, model2)
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seq_model.init_weights()
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# seq_model[0].conv1d.weight
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# Parameter containing:
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# tensor([[[0., 0., 0., 0.],
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# [0., 0., 0., 0.],
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# [0., 0., 0., 0.],
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# [0., 0., 0., 0.]]], requires_grad=True)
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# seq_model[1].conv2d.weight
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# Parameter containing:
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# tensor([[[[2., 2., 2.],
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# [2., 2., 2.],
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# [2., 2., 2.]],
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# ...,
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# [[2., 2., 2.],
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# [2., 2., 2.],
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# [2., 2., 2.]]]], requires_grad=True)
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# inner init_cfg has higher priority
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model1 = FooConv1d(init_cfg1)
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model2 = FooConv2d(init_cfg2)
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init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)
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seq_model = Sequential(model1, model2, init_cfg=init_cfg)
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seq_model.init_weights()
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# seq_model[0].conv1d.weight
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# Parameter containing:
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||
# tensor([[[0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.]]], requires_grad=True)
|
||
# seq_model[1].conv2d.weight
|
||
# Parameter containing:
|
||
# tensor([[[[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]],
|
||
# ...,
|
||
# [[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]]]], requires_grad=True)
|
||
|
||
# ModuleList
|
||
model1 = FooConv1d(init_cfg1)
|
||
model2 = FooConv2d(init_cfg2)
|
||
modellist = ModuleList([model1, model2])
|
||
modellist.init_weights()
|
||
# modellist[0].conv1d.weight
|
||
# Parameter containing:
|
||
# tensor([[[0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.]]], requires_grad=True)
|
||
# modellist[1].conv2d.weight
|
||
# Parameter containing:
|
||
# tensor([[[[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]],
|
||
# ...,
|
||
# [[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]]]], requires_grad=True)
|
||
|
||
# inner init_cfg has higher priority
|
||
model1 = FooConv1d(init_cfg1)
|
||
model2 = FooConv2d(init_cfg2)
|
||
init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)
|
||
modellist = ModuleList([model1, model2], init_cfg=init_cfg)
|
||
modellist.init_weights()
|
||
# modellist[0].conv1d.weight
|
||
# Parameter containing:
|
||
# tensor([[[0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.]]], requires_grad=True)
|
||
# modellist[1].conv2d.weight
|
||
# Parameter containing:
|
||
# tensor([[[[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]],
|
||
# ...,
|
||
# [[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]]]], requires_grad=True)
|
||
|
||
# ModuleDict
|
||
model1 = FooConv1d(init_cfg1)
|
||
model2 = FooConv2d(init_cfg2)
|
||
modeldict = ModuleDict(dict(model1=model1, model2=model2))
|
||
modeldict.init_weights()
|
||
# modeldict['model1'].conv1d.weight
|
||
# Parameter containing:
|
||
# tensor([[[0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.]]], requires_grad=True)
|
||
# modeldict['model2'].conv2d.weight
|
||
# Parameter containing:
|
||
# tensor([[[[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]],
|
||
# ...,
|
||
# [[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]]]], requires_grad=True)
|
||
|
||
# inner init_cfg has higher priority
|
||
model1 = FooConv1d(init_cfg1)
|
||
model2 = FooConv2d(init_cfg2)
|
||
init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d'], val=4., bias=5.)
|
||
modeldict = ModuleDict(dict(model1=model1, model2=model2), init_cfg=init_cfg)
|
||
modeldict.init_weights()
|
||
# modeldict['model1'].conv1d.weight
|
||
# Parameter containing:
|
||
# tensor([[[0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.],
|
||
# [0., 0., 0., 0.]]], requires_grad=True)
|
||
# modeldict['model2'].conv2d.weight
|
||
# Parameter containing:
|
||
# tensor([[[[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]],
|
||
# ...,
|
||
# [[2., 2., 2.],
|
||
# [2., 2., 2.],
|
||
# [2., 2., 2.]]]], requires_grad=True)
|
||
```
|
||
|
||
### Model Zoo
|
||
|
||
除了`torchvision`的预训练模型，我们还提供以下 CNN 的预训练模型：
|
||
|
||
- VGG Caffe
|
||
- ResNet Caffe
|
||
- ResNeXt
|
||
- ResNet with Group Normalization
|
||
- ResNet with Group Normalization and Weight Standardization
|
||
- HRNetV2
|
||
- Res2Net
|
||
- RegNet
|
||
|
||
#### Model URLs in JSON
|
||
|
||
MMCV中的Model Zoo Link 由 JSON 文件管理。 json 文件由模型名称及其url或path的键值对组成,一个json文件可能类似于:
|
||
|
||
```json
|
||
{
|
||
"model_a": "https://example.com/models/model_a_9e5bac.pth",
|
||
"model_b": "pretrain/model_b_ab3ef2c.pth"
|
||
}
|
||
```
|
||
|
||
可以在[此处](https://github.com/open-mmlab/mmcv/blob/master/mmcv/model_zoo/open_mmlab.json)找到托管在 OpenMMLab AWS 上的预训练模型的默认链接。
|
||
|
||
你可以通过将 `open-mmlab.json` 放在 `MMCV_HOME`下来覆盖默认链接，如果在环境中找不到`MMCV_HOME`，则默认使用 `~/.cache/mmcv`。当然你也可以使用命令 `export MMCV_HOME=/your/path`来设置自己的路径。
|
||
|
||
外部的json文件将被合并为默认文件，如果相同的键出现在外部`json`和默认`json`中，则将使用外部`json`。
|
||
|
||
#### Load Checkpoint
|
||
|
||
`mmcv.load_checkpoint()`的参数`filename`支持以下类型：
|
||
|
||
- filepath: `checkpoint`路径
|
||
- `http://xxx` and `https://xxx`: 下载checkpoint的链接，文件名中必需包含`SHA256`后缀
|
||
- `torchvision://xxx`: `torchvision.models`中的模型链接，更多细节参考 [torchvision](https://pytorch.org/docs/stable/torchvision/models.html)
|
||
- `open-mmlab://xxx`: 默认和其他 json 文件中提供的模型链接或文件路径
|