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Document_allmodules.md

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+# All modules and functions
+
+## 1 `wama_modules.BaseModule`
+
+### 1.1 Pooling
+ - `GlobalAvgPool` Global average pooling
+ - `GlobalMaxPool` Global maximum pooling
+ - `GlobalMaxAvgPool` GlobalMaxAvgPool = (GlobalAvgPool + GlobalMaxPool) / 2.
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+# import libs
+import torch
+from wama_modules.BaseModule import GlobalAvgPool, GlobalMaxPool, GlobalMaxAvgPool
+
+# make tensor
+inputs1D = torch.ones([3,12,13]) # 1D
+inputs2D = torch.ones([3,12,13,13]) # 2D
+inputs3D = torch.ones([3,12,13,13,13]) # 3D
+
+# build layer
+GAP = GlobalAvgPool()
+GMP = GlobalMaxPool()
+GAMP = GlobalMaxAvgPool()
+
+# test GAP & GMP & GAMP
+print(inputs1D.shape, GAP(inputs1D).shape)
+print(inputs2D.shape, GAP(inputs2D).shape)
+print(inputs3D.shape, GAP(inputs3D).shape)
+
+print(inputs1D.shape, GMP(inputs1D).shape)
+print(inputs2D.shape, GMP(inputs2D).shape)
+print(inputs3D.shape, GMP(inputs3D).shape)
+
+print(inputs1D.shape, GAMP(inputs1D).shape)
+print(inputs2D.shape, GAMP(inputs2D).shape)
+print(inputs3D.shape, GAMP(inputs3D).shape)
+```
+</details>
+
+
+### 1.2 Norm&Activation
+ - `customLayerNorm` a custom implementation of layer normalization
+ - `MakeNorm` make normalization layer, includes BN / GN / IN / LN
+ - `MakeActive` make activation layer, includes Relu / LeakyRelu
+ - `MakeConv` make 1D / 2D / 3D convolutional layer
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+```
+</details>
+
+
+
+### 1.3 Conv
+ - `ConvNormActive` 'Convolution→Normalization→Activation', used in VGG or ResNet
+ - `NormActiveConv` 'Normalization→Activation→Convolution', used in DenseNet
+ - `VGGBlock` the basic module in VGG
+ - `VGGStage` a VGGStage = few VGGBlocks
+ - `ResBlock` the basic module in ResNet
+ - `ResStage` a ResStage = few ResBlocks
+ - `DenseLayer` the basic module in DenseNet
+ - `DenseBlock` a DenseBlock = few DenseLayers
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+```
+</details>
+
+## 2 `wama_modules.utils`
+ - `resizeTensor` scale torch tensor, similar to scipy's zoom
+ - `tensor2array` transform tensor to ndarray
+ - `load_weights` load torch weights and print loading details(miss keys and match keys)
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+```
+</details>
+
+
+## 3 `wama_modules.Attention`
+ - `SCSEModule`
+ - `NonLocal`
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+```
+</details>
+
+
+## 4 `wama_modules.Encoder`
+ - `VGGEncoder`
+ - `ResNetEncoder`
+ - `DenseNetEncoder`
+ - `???`
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+```
+</details>
+
+
+## 5 `wama_modules.Decoder`
+ - `UNet_decoder`
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+```
+</details>
+
+
+## 6 `wama_modules.Neck`
+ - `FPN`
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+import torch
+from wama_modules.Neck import FPN
+
+# make multi-scale feature maps
+featuremaps = [
+ torch.ones([3,16,32,32,32]),
+ torch.ones([3,32,24,24,24]),
+ torch.ones([3,64,16,16,16]),
+ torch.ones([3,128,8,8,8]),
+]
+
+# build FPN
+fpn_AddSmall2Big = FPN(in_channels_list=[16, 32, 64, 128],
+ c1=128,
+ c2=256,
+ active='relu',
+ norm='bn',
+ gn_c=8,
+ mode='AddSmall2Big',
+ dim=3,)
+fpn_AddBig2Small = FPN(in_channels_list=[16, 32, 64, 128],
+ c1=128,
+ c2=256,
+ active='relu',
+ norm='bn',
+ gn_c=8,
+ mode='AddBig2Small', # Add big size feature to small size feature, for classification
+ dim=3,)
+
+# forward
+f_listA = fpn_AddSmall2Big(featuremaps)
+f_listB = fpn_AddBig2Small(featuremaps)
+_ = [print(i.shape) for i in featuremaps]
+_ = [print(i.shape) for i in f_listA]
+_ = [print(i.shape) for i in f_listB]
+```
+</details>
+
+
+## 7 `wama_modules.Transformer`
+ - `FeedForward`
+ - `MultiHeadAttention`
+ - `TransformerEncoderLayer`
+ - `TransformerDecoderLayer`
+
+<details>
+<summary> Click here to see demo code </summary>
+
+```python
+""" demo """
+```
+</details>