add

README.md

@@ -21,24 +21,58 @@ pip install git+https://github.com/WAMAWAMA/wama_modules.git
 
  Or you can directly copy the *wama_modules* folder to use
 
-
  - 💧1.2 [`segmentation_models_pytorch`](https://github.com/qubvel/segmentation_models.pytorch) (*Optional*) 
 
-*segmentation_models_pytorch* (called *smp*)
+<details>
+<summary> Introduction and installation command </summary>
+
+`segmentation_models_pytorch` (called *smp*)
 is a 2D CNN lib includes many backbones and decoders, which is highly recommended to install for cooperating with this library. 
-Install *smp* use ↓
+
+Install with pip：
+```
+pip install segmentation-models-pytorch
 ```
-pip install git+https://github.com/qubvel/segmentation_models.pytorch
+Install the latest version:
 ```
+pip install git+https://github.com/rwightman/pytorch-image-models.git
+```
+
+</details>
 
  - 💧1.3 [`transformers`](https://github.com/huggingface/transformers) (*Optional*) 
-
-*transformer* is a lib includes abundant Transformer structures, which is highly recommended to install for cooperating with this library. 
+
+<details>
+<summary> Introduction and installation command </summary>
+
+`transformer` is a lib includes abundant Transformer structures, which is highly recommended to install for cooperating with this library. 
 Install *transformer* use ↓
 ```
 pip install transformers
 ```
 
+
+</details>
+
+ - 💧1.4 [`timm`](https://github.com/rwightman/pytorch-image-models) (*Optional*) 
+
+<details>
+<summary> Introduction and installation command </summary>
+
+`timm`*` is a lib includes abundant CNN and Transformer structures, which is highly recommended to install for cooperating with this library. 
+Install *transformer* use ↓
+
+Install with pip:
+```
+pip install timm
+```
+Install the latest version:
+```
+pip install git+https://github.com/rwightman/pytorch-image-models.git
+```
+</details>
+
+
 ## 2. Update list
  - 2022/11/5: Open the source code, version `v0.0.1-beta`
  - ...
@@ -81,7 +115,7 @@ input = torch.ones([3,3,128,128])
 
 
 
-Here are more demos shown below ↓ (Click to view codes), or you can visit the `demo` folder for more demo codes
+Here are more demos shown below ↓ (Click to view codes, or visit the `demo` folder)
 
 
 
@@ -339,7 +373,37 @@ input = torch.ones([3,3,128,128])
 
 
 ## 5. Guideline 2: Use pretrained weights
-How to use pretrained weights? 
+
+All pretrained weights are from third-party codes or repos
+
+current pretrained support: (这里给一个表格，来自哪里，多少权重，预训练数据类型，2D还是3D）)
+ - 2D: smp, timm, radimagenet...
+ - 3D: medicalnet, 3D resnet, 3D densenet...
+
+
+### 5.1 smp encoders `2D`
+???
+
+### 5.2 timm encoders `2D`
+???
+
+### 5.2 radimagenet `2D` `medical image`
+???
+
+
+### 5.3 ??? `3D` `video`
+???
+
+### 5.3 ??? `3D` `video`
+???
+
+### 5.3 ??? `3D` `video`
+???
+
+### 5.3 ??? `3D` `medical image`
+???
+
+
 
 
 
@@ -536,12 +600,12 @@ _ = [print(i.shape) for i in f_listB]
 </details>
 
 
-## 7. Acknowledgment
-Thanks to ......
+## 7. Acknowledgment 🥰
+Thanks to these authors and their codes:
 1) https://github.com/ZhugeKongan/torch-template-for-deep-learning
 2) pytorch vit
-3) smp
+3) SMP: https://github.com/qubvel/segmentation_models.pytorch
 4) transformers
 5) medicalnet
-6)
+6) timm: https://github.com/rwightman/pytorch-image-models
 

tmp.py

@@ -11,3 +11,27 @@
 print(1)
 print(1)
 print(1)
+
+import torch
+from wama_modules.thirdparty_lib.ResNets3D_kenshohara.models.resnet import generate_model
+m = generate_model(18, n_classes = 1039)
+m.load_state_dict(torch.load(r"D:\pretrainedweights\kenshohara_ResNets3D\weights\r3d18_KM_200ep.pth", map_location='cpu')['state_dict'])
+
+
+
+
+import torch
+from wama_modules.thirdparty_lib.SMP_qubvel.encoders import get_encoder
+m = get_encoder('resnet18', in_channels=3, depth=5, weights='ssl')
+m = get_encoder('name', in_channels=3, depth=5, weights='ssl')
+m = get_encoder('resnet18', in_channels=3, depth=5, weights='ss')
+f_list = m(torch.ones([2,3,128,128]))
+_ = [print(i.shape) for i in f_list]
+
+import timm
+m = timm.create_model(
+ 'adv_inception_v3',
+ features_only=True,
+ pretrained=False,)
+f_list = m(torch.ones([2,3,128,128]))
+_ = [print(i.shape) for i in f_list]

wama_modules/Decoder.py

@@ -121,7 +121,7 @@ def forward(self, f_list):
 # try this https://blog.csdn.net/m0_51436734/article/details/124073901
 
 
-# NestedUNet
+# NestedUNet(Unet++)
 
 
 

wama_modules/thirdparty_lib/C3D_jfzhang95/c3d.py

@@ -0,0 +1,153 @@
+import torch
+import torch.nn as nn
+from mypath import Path
+
+class C3D(nn.Module):
+ """
+ The C3D network.
+ """
+
+ def __init__(self, num_classes, pretrained=False):
+ super(C3D, self).__init__()
+
+ self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
+
+ self.conv2 = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+
+ self.conv3a = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.conv3b = nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+
+ self.conv4a = nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.conv4b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+
+ self.conv5a = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.conv5b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1))
+
+ self.fc6 = nn.Linear(8192, 4096)
+ self.fc7 = nn.Linear(4096, 4096)
+ self.fc8 = nn.Linear(4096, num_classes)
+
+ self.dropout = nn.Dropout(p=0.5)
+
+ self.relu = nn.ReLU()
+
+ self.__init_weight()
+
+ if pretrained:
+ self.__load_pretrained_weights()
+
+ def forward(self, x):
+
+ x = self.relu(self.conv1(x))
+ x = self.pool1(x)
+
+ x = self.relu(self.conv2(x))
+ x = self.pool2(x)
+
+ x = self.relu(self.conv3a(x))
+ x = self.relu(self.conv3b(x))
+ x = self.pool3(x)
+
+ x = self.relu(self.conv4a(x))
+ x = self.relu(self.conv4b(x))
+ x = self.pool4(x)
+
+ x = self.relu(self.conv5a(x))
+ x = self.relu(self.conv5b(x))
+ x = self.pool5(x)
+
+ x = x.view(-1, 8192)
+ x = self.relu(self.fc6(x))
+ x = self.dropout(x)
+ x = self.relu(self.fc7(x))
+ x = self.dropout(x)
+
+ logits = self.fc8(x)
+
+ return logits
+
+ def __load_pretrained_weights(self):
+ """Initialiaze network."""
+ corresp_name = {
+ # Conv1
+ "features.0.weight": "conv1.weight",
+ "features.0.bias": "conv1.bias",
+ # Conv2
+ "features.3.weight": "conv2.weight",
+ "features.3.bias": "conv2.bias",
+ # Conv3a
+ "features.6.weight": "conv3a.weight",
+ "features.6.bias": "conv3a.bias",
+ # Conv3b
+ "features.8.weight": "conv3b.weight",
+ "features.8.bias": "conv3b.bias",
+ # Conv4a
+ "features.11.weight": "conv4a.weight",
+ "features.11.bias": "conv4a.bias",
+ # Conv4b
+ "features.13.weight": "conv4b.weight",
+ "features.13.bias": "conv4b.bias",
+ # Conv5a
+ "features.16.weight": "conv5a.weight",
+ "features.16.bias": "conv5a.bias",
+ # Conv5b
+ "features.18.weight": "conv5b.weight",
+ "features.18.bias": "conv5b.bias",
+ # fc6
+ "classifier.0.weight": "fc6.weight",
+ "classifier.0.bias": "fc6.bias",
+ # fc7
+ "classifier.3.weight": "fc7.weight",
+ "classifier.3.bias": "fc7.bias",
+ }
+
+ p_dict = torch.load(Path.model_dir())
+ s_dict = self.state_dict()
+ for name in p_dict:
+ if name not in corresp_name:
+ continue
+ s_dict[corresp_name[name]] = p_dict[name]
+ self.load_state_dict(s_dict)
+
+ def __init_weight(self):
+ for m in self.modules():
+ if isinstance(m, nn.Conv3d):
+ # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+ # m.weight.data.normal_(0, math.sqrt(2. / n))
+ torch.nn.init.kaiming_normal_(m.weight)
+ elif isinstance(m, nn.BatchNorm3d):
+ m.weight.data.fill_(1)
+ m.bias.data.zero_()
+
+def get_1x_lr_params(model):
+ """
+ This generator returns all the parameters for conv and two fc layers of the net.
+ """
+ b = [model.conv1, model.conv2, model.conv3a, model.conv3b, model.conv4a, model.conv4b,
+ model.conv5a, model.conv5b, model.fc6, model.fc7]
+ for i in range(len(b)):
+ for k in b[i].parameters():
+ if k.requires_grad:
+ yield k
+
+def get_10x_lr_params(model):
+ """
+ This generator returns all the parameters for the last fc layer of the net.
+ """
+ b = [model.fc8]
+ for j in range(len(b)):
+ for k in b[j].parameters():
+ if k.requires_grad:
+ yield k
+
+if __name__ == "__main__":
+ inputs = torch.rand(1, 3, 16, 112, 112)
+ net = C3D(num_classes=101, pretrained=True)
+
+ outputs = net.forward(inputs)
+ print(outputs.size())

wama_modules/thirdparty_lib/C3D_yyuanad/c3d.py

@@ -0,0 +1,72 @@
+# coding: utf-8
+
+import torch.nn as nn
+
+
+class C3D(nn.Module):
+ """
+ nb_classes: nb_classes in classification task, 101 for UCF101 dataset
+ """
+
+ def __init__(self, nb_classes):
+ super(C3D, self).__init__()
+
+ self.conv1 = nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool1 = nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2))
+
+ self.conv2 = nn.Conv3d(64, 128, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool2 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+
+ self.conv3a = nn.Conv3d(128, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.conv3b = nn.Conv3d(256, 256, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool3 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+
+ self.conv4a = nn.Conv3d(256, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.conv4b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool4 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2))
+
+ self.conv5a = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.conv5b = nn.Conv3d(512, 512, kernel_size=(3, 3, 3), padding=(1, 1, 1))
+ self.pool5 = nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 1, 1))
+
+ self.fc6 = nn.Linear(8192, 4096)
+ self.fc7 = nn.Linear(4096, 4096)
+ self.fc8 = nn.Linear(4096, nb_classes)
+
+ self.dropout = nn.Dropout(p=0.5)
+
+ self.relu = nn.ReLU()
+
+ def forward(self, x, feature_layer):
+ h = self.relu(self.conv1(x))
+ h = self.pool1(h)
+ h = self.relu(self.conv2(h))
+ h = self.pool2(h)
+
+ h = self.relu(self.conv3a(h))
+ h = self.relu(self.conv3b(h))
+ h = self.pool3(h)
+
+ h = self.relu(self.conv4a(h))
+ h = self.relu(self.conv4b(h))
+ h = self.pool4(h)
+
+ h = self.relu(self.conv5a(h))
+ h = self.relu(self.conv5b(h))
+ h = self.pool5(h)
+
+ h = h.view(-1, 8192)
+ out = h if feature_layer == 5 else None
+ h = self.relu(self.fc6(h))
+ out = h if feature_layer == 6 and out == None else out
+ h = self.dropout(h)
+ h = self.relu(self.fc7(h))
+ out = h if feature_layer == 7 and out == None else out
+ h = self.dropout(h)
+ logits = self.fc8(h)
+ return logits, out
+
+
+
+
+

wama_modules/thirdparty_lib/MedicalNet_Tencent/.commitlintrc.yml

@@ -0,0 +1,2 @@
+extends:
+ - "@commitlint/config-conventional"