commit BiRefNetUltra node
This commit is contained in:
chflame
@@ -87,6 +87,7 @@ When this error has occurred, please check the network environment.
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## Update
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<font size="4">**If the dependency package error after updating, please reinstall the relevant dependency packages. for details, please refer to [here](https://github.com/chflame163/ComfyUI_LayerStyle/issues/5). </font><br />
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* Commit [BiRefNetUltra](#BiRefNetUltra) node, it using the BiRefNet model to remove background has better recognition ability and ultra-high edge details.
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* Commit [ImageAutoCropV2](#ImageAutoCropV2) node, it can choose not to remove the background, support mask input, and scale by long or short side size.
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* Commit [ImageHub](#ImageHub) node, supports up to 9 sets of Image and Mask switching output, and supports random output.
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* Commit [TextJoin](#TextJoin) node.
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@@ -627,7 +628,7 @@ Node option:
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* [note](#notes)
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### <a id="table1">ColorPicker</a>
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Modify web extensions from [mtb nodes](https://github.com/melMass/comfy_mtb). Select colors on the color palette and output RGB values.
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Modify web extensions from [mtb nodes](https://github.com/melMass/comfy_mtb). Select colors on the color palette and output RGB values, thanks to the original author.
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Node options:
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@@ -752,7 +753,7 @@ The following changes have been made based on GradientImage:
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### <a id="table1">SimpleTextImage</a>
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Generate simple typesetting images and masks from text. This node references some of the functionalities and code of [ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite](https://github.com/ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite).
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Generate simple typesetting images and masks from text. This node references some of the functionalities and code of [ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite](https://github.com/ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite), thanks to the original author.
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Node options:
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@@ -809,7 +810,7 @@ If the folder set in ini is invalid, the font folder that comes with the plugin
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### <a id="table1">LaMa</a>
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Erase objects from the image based on the mask. this node is repackage of [IOPaint](https://www.iopaint.com), powered by state-of-the-art AI models.
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Erase objects from the image based on the mask. this node is repackage of [IOPaint](https://www.iopaint.com), powered by state-of-the-art AI models, thanks to the original author.
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It is have [LaMa](https://github.com/advimman/lama), [LDM](https://github.com/CompVis/latent-diffusion), [ZITS](https://github.com/DQiaole/ZITS_inpainting),[MAT](https://github.com/fenglinglwb/MAT), [FcF](https://github.com/SHI-Labs/FcF-Inpainting), [Manga](https://github.com/msxie92/MangaInpainting) models and the SPREAD method to erase. Please refer to the original link for the introduction of each model.
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Please download the model files from [lama models(Baidu Netdisk)](https://pan.baidu.com/s/1LllR9TJHP1G9uEwWT3Mvkg?pwd=tvzv) or [lama models(Google Drive)](https://drive.google.com/drive/folders/1Aq0a4sybb3SRxi7j1e1_ZbBRjaWDdP9e?usp=sharing) to ```ComfyUI/models/lama``` folder.
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@@ -945,7 +946,7 @@ Output:
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* y: The y-coordinate of the top left corner position.
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### <a id="table1">SegmentAnythingUltra</a>
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Improvements to [ComfyUI Segment Anything](https://github.com/storyicon/comfyui_segment_anything), combined with the Alpha Matte node of [ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters) in spacepxl, result in more detailed edges for masks.
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Improvements to [ComfyUI Segment Anything](https://github.com/storyicon/comfyui_segment_anything), combined with the Alpha Matte node of [ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters) in spacepxl, result in more detailed edges for masks, thanks to the original author.
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*Please refer to the installation of ComfyUI Segment Anything to install the model. If ComfyUI Segment Anything has been correctly installed, you can skip this step.
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* From [here](https://huggingface.co/bert-base-uncased/tree/main) download the config.json，model.safetensors，tokenizer_config.json，tokenizer.json and vocab.txt 5 files to ```ComfyUI/models/bert-base-uncased``` folder.
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@@ -984,7 +985,7 @@ On the basis of SegmentAnythingUltra, the following changes have been made:
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### <a id="table1">RemBgUltra</a>
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Remove background. compared to the similar background removal nodes, this node has ultra-high edge details.
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This node combines the Alpha Matte node of Spacepxl's [ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters) and the functionality of ZHO-ZHO-ZHO's [ComfyUI-BRIA_AI-RMBG](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BRIA_AI-RMBG).
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This node combines the Alpha Matte node of Spacepxl's [ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters) and the functionality of ZHO-ZHO-ZHO's [ComfyUI-BRIA_AI-RMBG](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BRIA_AI-RMBG), thanks to the original author.
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*Download the [BRIA Background Removal v1.4](https://huggingface.co/briaai/RMBG-1.4) model file (```model.pth```) to the ```/ComfyUI/models/rmbg/RMBG-1.4``` folder. This model was developed by BRIA AI and can be used as an open-source model for non-commercial purposes.
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**After downloading the model, recommended to add a line 'rmbg: models/rmbg/RMBG-1.4' in ComfyUI/extra_model_paths.yaml. (This step is not mandatory)
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@@ -1007,9 +1008,29 @@ On the basis of RemBgUltra, the following changes have been made:
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* detail_erode: Mask the erosion range inward from the edge. the larger the value, the larger the range of inward repair.
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* detail_dilate: The edge of the mask expands outward. the larger the value, the wider the range of outward repair.
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### <a id="table1">BiRefNetUltra</a>
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Using the BiRefNet model to remove background has better recognition ability and ultra-high edge details.
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The code for the model part of this node comes from Viper's [ComfyUI-BiRefNet](https://github.com/viperyl/ComfyUI-BiRefNet)，thanks to the original author.
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*From [https://huggingface.co/ViperYX/BiRefNet](https://huggingface.co/ViperYX/BiRefNet/tree/main) download the ```BiRefNet-ep480.pth```,```pvt_v2_b2.pth```,```pvt_v2_b5.pth```,```swin_base_patch4_window12_384_22kto1k.pth```, ```swin_large_patch4_window12_384_22kto1k.pth``` 5 files to ```ComfyUI/models/BiRefNet``` folder.
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Node options:
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* detail_method: Edge processing methods. provides three methods: VITMatte, PyMatting and GuidedFilter.
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* detail_erode: Mask the erosion range inward from the edge. the larger the value, the larger the range of inward repair.
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* detail_dilate: The edge of the mask expands outward. the larger the value, the wider the range of outward repair.
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* black_point: Edge black sampling threshold.
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* white_point: Edge white sampling threshold.
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* process_detail: Set to false here will skip edge processing to save runtime.
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### <a id="table1">PersonMaskUltra</a>
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Generate masks for portrait's face, hair, body skin, clothing, or accessories. Compared to the previous A Person Mask Generator node, this node has ultra-high edge details.
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The model code for this node comes from [a-person-mask-generator](https://github.com/djbielejeski/a-person-mask-generator)， edge processing code from [ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters).
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The model code for this node comes from [a-person-mask-generator](https://github.com/djbielejeski/a-person-mask-generator)， edge processing code from [ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)，thanks to the original author.
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@@ -1074,7 +1095,7 @@ Node options:
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### <a id="table1">MaskEdgeUltraDetail</a>
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Process rough masks to ultra fine edges.
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This node combines the Alpha Matte and the Guided Filter Alpha nodes functions of Spacepxl's [ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters).
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This node combines the Alpha Matte and the Guided Filter Alpha nodes functions of Spacepxl's [ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters), thanks to the original author.
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Node options:
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@@ -1239,7 +1260,7 @@ Node options:
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### <a id="table1">Film</a>
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Simulate the grain, dark edge, and blurred edge of the film, support input depth map to simulate defocus.
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This node is reorganize and encapsulate of [digitaljohn/comfyui-propost](https://github.com/digitaljohn/comfyui-propost).
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This node is reorganize and encapsulate of [digitaljohn/comfyui-propost](https://github.com/digitaljohn/comfyui-propost), thanks to the original author.
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Node options:
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@@ -1309,4 +1330,4 @@ Node options:
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<sup>5</sup> The layer_image and layer_mask must be of the same size.
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# statement
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LayerStyle nodes follows the MIT license, Some of its functional code comes from other open-source projects. If used for commercial purposes, please refer to the original project license to authorization agreement.
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LayerStyle nodes follows the MIT license, Some of its functional code comes from other open-source projects. Thanks to the original author. If used for commercial purposes, please refer to the original project license to authorization agreement.
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+28
-11
@@ -88,6 +88,7 @@ git clone https://github.com/chflame163/ComfyUI_LayerStyle.git
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## 更新说明
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<font size="4">**如果本插件更新后出现依赖包错误，请重新安装相关依赖包。详情见[这里](https://github.com/chflame163/ComfyUI_LayerStyle/issues/5)。 </font><br />
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* 添加 [BiRefNetUltra](#BiRefNetUltra) 节点, 使用BiRefNet模型去除背景，有更好的识别能力，同时具有超高的边缘细节。
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* 添加 [ImageAutoCropV2](#ImageAutoCropV2) 节点，可选择不去除背景，支持mask输入，支持按长边或短边尺寸缩放。
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* 添加 [ImageHub](#ImageHub) 节点，支持最多9组Image和Mask切换，支持随机输出。
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* 添加 [TextJoin](#TextJoin) 节点。
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@@ -625,7 +626,7 @@ ImageScaleByAspectRatio的V2升级版
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### <a id="table1">ColorPicker</a>
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在色板上选取颜色并输出。
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改自[mtb nodes](https://github.com/melMass/comfy_mtb)的web extensions。
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改自[mtb nodes](https://github.com/melMass/comfy_mtb)的web extensions，感谢原作者。
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节点选项说明:
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@@ -751,7 +752,7 @@ GradientImage的V2升级版。
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### <a id="table1">SimpleTextImage</a>
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从文字生成简单排版的图片以及遮罩。这个节点参考了[ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite](https://github.com/ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite)的部分功能和代码。
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从文字生成简单排版的图片以及遮罩。这个节点参考了[ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite](https://github.com/ZHO-ZHO-ZHO/ComfyUI-Text_Image-Composite)的部分功能和代码，感谢原作者。
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节点选项说明:
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@@ -805,7 +806,7 @@ GradientImage的V2升级版。
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### <a id="table1">LaMa</a>
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根据图像遮罩擦除物体。本节点是对[IOPaint](https://www.iopaint.com)的封装，由 SOTA AI 模型提供支持。
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根据图像遮罩擦除物体。本节点是对[IOPaint](https://www.iopaint.com)的封装，由 SOTA AI 模型提供支持， 感谢原作者。
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提供[LaMa](https://github.com/advimman/lama), [LDM](https://github.com/CompVis/latent-diffusion), [ZITS](https://github.com/DQiaole/ZITS_inpainting),[MAT](https://github.com/fenglinglwb/MAT), [FcF](https://github.com/SHI-Labs/FcF-Inpainting), [Manga](https://github.com/msxie92/MangaInpainting) 模型以及 SPREAD 擦除方法。请查看链接了解各个模型的介绍。
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请下载模型文件 [lama models(百度网盘)](https://pan.baidu.com/s/1LllR9TJHP1G9uEwWT3Mvkg?pwd=tvzv) 或者 [lama models(Google Drive)](https://drive.google.com/drive/folders/1Aq0a4sybb3SRxi7j1e1_ZbBRjaWDdP9e?usp=sharing), 将文件放到```ComfyUI/models/lama```
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@@ -943,7 +944,7 @@ cropped_mask: 裁切后的遮罩。
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* y: 左上角位置y坐标输出。
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### <a id="table1">SegmentAnythingUltra</a>
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对[ComfyUI Segment Anything](https://github.com/storyicon/comfyui_segment_anything)的改进，结合了spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)的Alpha Matte节点，使遮罩有更具细节的边缘。
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对[ComfyUI Segment Anything](https://github.com/storyicon/comfyui_segment_anything)的改进，结合了spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)的Alpha Matte节点，使遮罩有更具细节的边缘，感谢原作者。
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*请参照ComfyUI Segment Anything的安装方法安装模型。如果已经正确安装了ComfyUI Segment Anything，可跳过此步骤。
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* 从 [这里](https://huggingface.co/bert-base-uncased/tree/main) 下载 config.json，model.safetensors，tokenizer_config.json，tokenizer.json 和 vocab.txt 5个文件到 ```ComfyUI/models/bert-base-uncased```文件夹。
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@@ -980,14 +981,13 @@ SegmentAnythingUltra的V2升级版，增加了VITMatte边缘处理方法。(注
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* detail_dilate: 遮罩边缘向外扩张范围。数值越大，向外修复的范围越大。
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### <a id="table1">RemBgUltra</a>
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去除背景。与类似的背景移除节点相比，这个节点具有超高的边缘细节。
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本节点结合了spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)的Alpha Matte节点，以及ZHO-ZHO-ZHO的[ComfyUI-BRIA_AI-RMBG](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BRIA_AI-RMBG)的功能。
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本节点结合了spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)的Alpha Matte节点，以及ZHO-ZHO-ZHO的[ComfyUI-BRIA_AI-RMBG](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BRIA_AI-RMBG)的功能，感谢原作者。
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*将[BRIA Background Removal v1.4](https://huggingface.co/briaai/RMBG-1.4)模型文件(model.pth)下载至```ComfyUI/models/rmbg/RMBG-1.4```文件夹。该模型由 BRIA AI 开发，可作为非商业用途的开源模型。
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**下载模型后，建议在ComfyUI/extra_model_paths.yaml中添加一行'rmbg: models/rmbg/RMBG-1.4'。(此步骤非必须）
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@@ -1007,10 +1007,27 @@ RemBgUltra的V2升级版，增加了VITMatte边缘处理方法。(注意:超过2
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* detail_erode: 遮罩边缘向内侵蚀范围。数值越大，向内修复的范围越大。
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* detail_dilate: 遮罩边缘向外扩张范围。数值越大，向外修复的范围越大。
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### <a id="table1">BiRefNetUltra</a>
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使用BiRefNet模型去除背景，有更好的识别能力，同时具有超高的边缘细节。
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本节点模型部分的代码来自vipery的[ComfyUI-BiRefNet](https://github.com/viperyl/ComfyUI-BiRefNet)，感谢原作者。
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*从[https://huggingface.co/ViperYX/BiRefNet](https://huggingface.co/ViperYX/BiRefNet/tree/main)下载```BiRefNet-ep480.pth```,```pvt_v2_b2.pth```,```pvt_v2_b5.pth```,```swin_base_patch4_window12_384_22kto1k.pth```, ```swin_large_patch4_window12_384_22kto1k.pth```5个文件至```ComfyUI/models/BiRefNet```文件夹。
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节点选项说明:
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* detail_method: 边缘处理方法。提供了VITMatte, PyMatting, GuidedFilter三种方法。
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* detail_erode: 遮罩边缘向内侵蚀范围。数值越大，向内修复的范围越大。
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* detail_dilate: 遮罩边缘向外扩张范围。数值越大，向外修复的范围越大。
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* black_point: 边缘黑色采样阈值。
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* white_point: 边缘黑色采样阈值。
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* process_detail: 此处设为False将跳过边缘处理以节省运行时间。
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### <a id="table1">PersonMaskUltra</a>
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为人物生成脸、头发、身体皮肤、衣服或配饰的遮罩。与之前的A Person Mask Generator节点相比，这个节点具有超高的边缘细节。
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本节点的模型代码来自[a-person-mask-generator](https://github.com/djbielejeski/a-person-mask-generator)，边缘处理代码来自spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)。
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本节点的模型代码来自[a-person-mask-generator](https://github.com/djbielejeski/a-person-mask-generator)，边缘处理代码来自spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)，感谢原作者。
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@@ -1076,7 +1093,7 @@ PersonMaskUltra的V2升级版，增加了VITMatte边缘处理方法。(注意:
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### <a id="table1">MaskEdgeUltraDetail</a>
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处理较粗糙的遮罩使其获得超精细边缘。
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本节点结合了spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)的Alpha Matte和Guided Filter Alpha两者功能。
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本节点结合了spacepxl的[ComfyUI-Image-Filters](https://github.com/spacepxl/ComfyUI-Image-Filters)的Alpha Matte和Guided Filter Alpha两者功能，感谢原作者。
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节点选项说明:
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@@ -1242,7 +1259,7 @@ mask反转
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### <a id="table1">Film</a>
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模拟胶片的颗粒、暗边和边缘模糊，支持输入深度图模拟虚焦。
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这个节点是[digitaljohn/comfyui-propost](https://github.com/digitaljohn/comfyui-propost)的重新封装。
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这个节点是[digitaljohn/comfyui-propost](https://github.com/digitaljohn/comfyui-propost)的重新封装，感谢原作者。
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节点选项说明:
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@@ -1314,4 +1331,4 @@ mask反转
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## 声明
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LayerStyle节点遵照MIT开源协议，有部分功能代码和模型来自其他开源项目。如果作为商业用途，请查阅原项目授权协议使用。
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LayerStyle节点遵照MIT开源协议，有部分功能代码和模型来自其他开源项目，感谢原作者。如果作为商业用途，请查阅原项目授权协议使用。
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@@ -0,0 +1,44 @@
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import torch
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import torch.nn as nn
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from collections import OrderedDict
|
||||
from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights
|
||||
from BiRefNet.backbones.pvt_v2 import pvt_v2_b2, pvt_v2_b5
|
||||
from BiRefNet.backbones.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l
|
||||
from ..config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
def build_backbone(bb_name, pretrained=True, params_settings=''):
|
||||
if bb_name == 'vgg16':
|
||||
bb_net = list(vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children())[0]
|
||||
bb = nn.Sequential(OrderedDict({'conv1': bb_net[:4], 'conv2': bb_net[4:9], 'conv3': bb_net[9:16], 'conv4': bb_net[16:23]}))
|
||||
elif bb_name == 'vgg16bn':
|
||||
bb_net = list(vgg16_bn(pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None).children())[0]
|
||||
bb = nn.Sequential(OrderedDict({'conv1': bb_net[:6], 'conv2': bb_net[6:13], 'conv3': bb_net[13:23], 'conv4': bb_net[23:33]}))
|
||||
elif bb_name == 'resnet50':
|
||||
bb_net = list(resnet50(pretrained=ResNet50_Weights.DEFAULT if pretrained else None).children())
|
||||
bb = nn.Sequential(OrderedDict({'conv1': nn.Sequential(*bb_net[0:3]), 'conv2': bb_net[4], 'conv3': bb_net[5], 'conv4': bb_net[6]}))
|
||||
else:
|
||||
bb = eval('{}({})'.format(bb_name, params_settings))
|
||||
if pretrained:
|
||||
bb = load_weights(bb, bb_name)
|
||||
return bb
|
||||
|
||||
def load_weights(model, model_name):
|
||||
save_model = torch.load(config.weights[model_name])
|
||||
model_dict = model.state_dict()
|
||||
state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model.items() if k in model_dict.keys()}
|
||||
# to ignore the weights with mismatched size when I modify the backbone itself.
|
||||
if not state_dict:
|
||||
save_model_keys = list(save_model.keys())
|
||||
sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None
|
||||
state_dict = {k: v if v.size() == model_dict[k].size() else model_dict[k] for k, v in save_model[sub_item].items() if k in model_dict.keys()}
|
||||
if not state_dict or not sub_item:
|
||||
print('Weights are not successully loaded. Check the state dict of weights file.')
|
||||
return None
|
||||
else:
|
||||
print('Found correct weights in the "{}" item of loaded state_dict.'.format(sub_item))
|
||||
model_dict.update(state_dict)
|
||||
model.load_state_dict(model_dict)
|
||||
return model
|
||||
@@ -0,0 +1,434 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from functools import partial
|
||||
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
from timm.models.registry import register_model
|
||||
|
||||
import math
|
||||
|
||||
from ..config import Config
|
||||
config = Config()
|
||||
|
||||
class Mlp(nn.Module):
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.dwconv = DWConv(hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = self.fc1(x)
|
||||
x = self.dwconv(x, H, W)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
|
||||
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
self.q = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
|
||||
self.attn_drop_prob = attn_drop
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
self.sr_ratio = sr_ratio
|
||||
if sr_ratio > 1:
|
||||
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
|
||||
if self.sr_ratio > 1:
|
||||
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
|
||||
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
|
||||
x_ = self.norm(x_)
|
||||
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
else:
|
||||
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
k, v = kv[0], kv[1]
|
||||
|
||||
if config.SDPA_enabled:
|
||||
x = torch.nn.functional.scaled_dot_product_attention(
|
||||
q, k, v,
|
||||
attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
|
||||
).transpose(1, 2).reshape(B, N, C)
|
||||
else:
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = Attention(
|
||||
dim,
|
||||
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class OverlapPatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
|
||||
self.num_patches = self.H * self.W
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
|
||||
padding=(patch_size[0] // 2, patch_size[1] // 2))
|
||||
self.norm = nn.LayerNorm(embed_dim)
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
_, _, H, W = x.shape
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
|
||||
return x, H, W
|
||||
|
||||
|
||||
class PyramidVisionTransformerImpr(nn.Module):
|
||||
def __init__(self, img_size=224, patch_size=16, in_channels=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
|
||||
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
|
||||
attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm,
|
||||
depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
|
||||
super().__init__()
|
||||
self.num_classes = num_classes
|
||||
self.depths = depths
|
||||
|
||||
# patch_embed
|
||||
self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_channels=in_channels,
|
||||
embed_dim=embed_dims[0])
|
||||
self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_channels=embed_dims[0],
|
||||
embed_dim=embed_dims[1])
|
||||
self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_channels=embed_dims[1],
|
||||
embed_dim=embed_dims[2])
|
||||
self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_channels=embed_dims[2],
|
||||
embed_dim=embed_dims[3])
|
||||
|
||||
# transformer encoder
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
cur = 0
|
||||
self.block1 = nn.ModuleList([Block(
|
||||
dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[0])
|
||||
for i in range(depths[0])])
|
||||
self.norm1 = norm_layer(embed_dims[0])
|
||||
|
||||
cur += depths[0]
|
||||
self.block2 = nn.ModuleList([Block(
|
||||
dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[1])
|
||||
for i in range(depths[1])])
|
||||
self.norm2 = norm_layer(embed_dims[1])
|
||||
|
||||
cur += depths[1]
|
||||
self.block3 = nn.ModuleList([Block(
|
||||
dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[2])
|
||||
for i in range(depths[2])])
|
||||
self.norm3 = norm_layer(embed_dims[2])
|
||||
|
||||
cur += depths[2]
|
||||
self.block4 = nn.ModuleList([Block(
|
||||
dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
|
||||
sr_ratio=sr_ratios[3])
|
||||
for i in range(depths[3])])
|
||||
self.norm4 = norm_layer(embed_dims[3])
|
||||
|
||||
# classification head
|
||||
# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
self.apply(self._init_weights)
|
||||
|
||||
def _init_weights(self, m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
elif isinstance(m, nn.Conv2d):
|
||||
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
fan_out //= m.groups
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
|
||||
if m.bias is not None:
|
||||
m.bias.data.zero_()
|
||||
|
||||
def init_weights(self, pretrained=None):
|
||||
if isinstance(pretrained, str):
|
||||
logger = 1
|
||||
#load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
|
||||
|
||||
def reset_drop_path(self, drop_path_rate):
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
|
||||
cur = 0
|
||||
for i in range(self.depths[0]):
|
||||
self.block1[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[0]
|
||||
for i in range(self.depths[1]):
|
||||
self.block2[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[1]
|
||||
for i in range(self.depths[2]):
|
||||
self.block3[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
cur += self.depths[2]
|
||||
for i in range(self.depths[3]):
|
||||
self.block4[i].drop_path.drop_prob = dpr[cur + i]
|
||||
|
||||
def freeze_patch_emb(self):
|
||||
self.patch_embed1.requires_grad = False
|
||||
|
||||
@torch.jit.ignore
|
||||
def no_weight_decay(self):
|
||||
return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
|
||||
|
||||
def get_classifier(self):
|
||||
return self.head
|
||||
|
||||
def reset_classifier(self, num_classes, global_pool=''):
|
||||
self.num_classes = num_classes
|
||||
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
|
||||
|
||||
def forward_features(self, x):
|
||||
B = x.shape[0]
|
||||
outs = []
|
||||
|
||||
# stage 1
|
||||
x, H, W = self.patch_embed1(x)
|
||||
for i, blk in enumerate(self.block1):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm1(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 2
|
||||
x, H, W = self.patch_embed2(x)
|
||||
for i, blk in enumerate(self.block2):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm2(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 3
|
||||
x, H, W = self.patch_embed3(x)
|
||||
for i, blk in enumerate(self.block3):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm3(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
# stage 4
|
||||
x, H, W = self.patch_embed4(x)
|
||||
for i, blk in enumerate(self.block4):
|
||||
x = blk(x, H, W)
|
||||
x = self.norm4(x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(x)
|
||||
|
||||
return outs
|
||||
|
||||
# return x.mean(dim=1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.forward_features(x)
|
||||
# x = self.head(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class DWConv(nn.Module):
|
||||
def __init__(self, dim=768):
|
||||
super(DWConv, self).__init__()
|
||||
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
x = x.transpose(1, 2).view(B, C, H, W).contiguous()
|
||||
x = self.dwconv(x)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def _conv_filter(state_dict, patch_size=16):
|
||||
""" convert patch embedding weight from manual patchify + linear proj to conv"""
|
||||
out_dict = {}
|
||||
for k, v in state_dict.items():
|
||||
if 'patch_embed.proj.weight' in k:
|
||||
v = v.reshape((v.shape[0], 3, patch_size, patch_size))
|
||||
out_dict[k] = v
|
||||
|
||||
return out_dict
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b0(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b0, self).__init__(
|
||||
patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b1(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b1, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b2(PyramidVisionTransformerImpr):
|
||||
def __init__(self, in_channels=3, **kwargs):
|
||||
super(pvt_v2_b2, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1, in_channels=in_channels)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b3(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b3, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b4(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b4, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
|
||||
|
||||
## @register_model
|
||||
class pvt_v2_b5(PyramidVisionTransformerImpr):
|
||||
def __init__(self, **kwargs):
|
||||
super(pvt_v2_b5, self).__init__(
|
||||
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
|
||||
qkv_bias=True, norm_layer=partial(nn.LayerNorm, eps=1e-6), depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
|
||||
drop_rate=0.0, drop_path_rate=0.1)
|
||||
@@ -0,0 +1,652 @@
|
||||
# --------------------------------------------------------
|
||||
# Swin Transformer
|
||||
# Copyright (c) 2021 Microsoft
|
||||
# Licensed under The MIT License [see LICENSE for details]
|
||||
# Written by Ze Liu, Yutong Lin, Yixuan Wei
|
||||
# --------------------------------------------------------
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint as checkpoint
|
||||
import numpy as np
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
|
||||
from ..config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
class Mlp(nn.Module):
|
||||
""" Multilayer perceptron."""
|
||||
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
def window_partition(x, window_size):
|
||||
"""
|
||||
Args:
|
||||
x: (B, H, W, C)
|
||||
window_size (int): window size
|
||||
|
||||
Returns:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
"""
|
||||
B, H, W, C = x.shape
|
||||
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
|
||||
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
|
||||
return windows
|
||||
|
||||
|
||||
def window_reverse(windows, window_size, H, W):
|
||||
"""
|
||||
Args:
|
||||
windows: (num_windows*B, window_size, window_size, C)
|
||||
window_size (int): Window size
|
||||
H (int): Height of image
|
||||
W (int): Width of image
|
||||
|
||||
Returns:
|
||||
x: (B, H, W, C)
|
||||
"""
|
||||
B = int(windows.shape[0] / (H * W / window_size / window_size))
|
||||
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
|
||||
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
|
||||
return x
|
||||
|
||||
|
||||
class WindowAttention(nn.Module):
|
||||
""" Window based multi-head self attention (W-MSA) module with relative position bias.
|
||||
It supports both of shifted and non-shifted window.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
window_size (tuple[int]): The height and width of the window.
|
||||
num_heads (int): Number of attention heads.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
|
||||
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
|
||||
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
|
||||
"""
|
||||
|
||||
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
|
||||
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.window_size = window_size # Wh, Ww
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
# define a parameter table of relative position bias
|
||||
self.relative_position_bias_table = nn.Parameter(
|
||||
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
|
||||
|
||||
# get pair-wise relative position index for each token inside the window
|
||||
coords_h = torch.arange(self.window_size[0])
|
||||
coords_w = torch.arange(self.window_size[1])
|
||||
coords = torch.stack(torch.meshgrid([coords_h, coords_w], indexing='ij')) # 2, Wh, Ww
|
||||
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
|
||||
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
|
||||
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
|
||||
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
|
||||
relative_coords[:, :, 1] += self.window_size[1] - 1
|
||||
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
|
||||
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
|
||||
self.register_buffer("relative_position_index", relative_position_index)
|
||||
|
||||
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
|
||||
self.attn_drop_prob = attn_drop
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
trunc_normal_(self.relative_position_bias_table, std=.02)
|
||||
self.softmax = nn.Softmax(dim=-1)
|
||||
|
||||
def forward(self, x, mask=None):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: input features with shape of (num_windows*B, N, C)
|
||||
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
|
||||
"""
|
||||
B_, N, C = x.shape
|
||||
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
|
||||
|
||||
q = q * self.scale
|
||||
|
||||
if config.SDPA_enabled:
|
||||
x = torch.nn.functional.scaled_dot_product_attention(
|
||||
q, k, v,
|
||||
attn_mask=None, dropout_p=self.attn_drop_prob, is_causal=False
|
||||
).transpose(1, 2).reshape(B_, N, C)
|
||||
else:
|
||||
attn = (q @ k.transpose(-2, -1))
|
||||
|
||||
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
|
||||
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
|
||||
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
|
||||
attn = attn + relative_position_bias.unsqueeze(0)
|
||||
|
||||
if mask is not None:
|
||||
nW = mask.shape[0]
|
||||
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
|
||||
attn = attn.view(-1, self.num_heads, N, N)
|
||||
attn = self.softmax(attn)
|
||||
else:
|
||||
attn = self.softmax(attn)
|
||||
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformerBlock(nn.Module):
|
||||
""" Swin Transformer Block.
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
num_heads (int): Number of attention heads.
|
||||
window_size (int): Window size.
|
||||
shift_size (int): Shift size for SW-MSA.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float, optional): Stochastic depth rate. Default: 0.0
|
||||
act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
|
||||
def __init__(self, dim, num_heads, window_size=7, shift_size=0,
|
||||
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
|
||||
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
self.window_size = window_size
|
||||
self.shift_size = shift_size
|
||||
self.mlp_ratio = mlp_ratio
|
||||
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
|
||||
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = WindowAttention(
|
||||
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
|
||||
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
|
||||
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
self.H = None
|
||||
self.W = None
|
||||
|
||||
def forward(self, x, mask_matrix):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
mask_matrix: Attention mask for cyclic shift.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
H, W = self.H, self.W
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
shortcut = x
|
||||
x = self.norm1(x)
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# pad feature maps to multiples of window size
|
||||
pad_l = pad_t = 0
|
||||
pad_r = (self.window_size - W % self.window_size) % self.window_size
|
||||
pad_b = (self.window_size - H % self.window_size) % self.window_size
|
||||
x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
|
||||
_, Hp, Wp, _ = x.shape
|
||||
|
||||
# cyclic shift
|
||||
if self.shift_size > 0:
|
||||
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
|
||||
attn_mask = mask_matrix
|
||||
else:
|
||||
shifted_x = x
|
||||
attn_mask = None
|
||||
|
||||
# partition windows
|
||||
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
|
||||
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
|
||||
|
||||
# W-MSA/SW-MSA
|
||||
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
|
||||
|
||||
# merge windows
|
||||
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
|
||||
shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
|
||||
|
||||
# reverse cyclic shift
|
||||
if self.shift_size > 0:
|
||||
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
|
||||
else:
|
||||
x = shifted_x
|
||||
|
||||
if pad_r > 0 or pad_b > 0:
|
||||
x = x[:, :H, :W, :].contiguous()
|
||||
|
||||
x = x.view(B, H * W, C)
|
||||
|
||||
# FFN
|
||||
x = shortcut + self.drop_path(x)
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x)))
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class PatchMerging(nn.Module):
|
||||
""" Patch Merging Layer
|
||||
|
||||
Args:
|
||||
dim (int): Number of input channels.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
"""
|
||||
def __init__(self, dim, norm_layer=nn.LayerNorm):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
|
||||
self.norm = norm_layer(4 * dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
B, L, C = x.shape
|
||||
assert L == H * W, "input feature has wrong size"
|
||||
|
||||
x = x.view(B, H, W, C)
|
||||
|
||||
# padding
|
||||
pad_input = (H % 2 == 1) or (W % 2 == 1)
|
||||
if pad_input:
|
||||
x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
|
||||
|
||||
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
|
||||
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
|
||||
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
|
||||
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
|
||||
x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
|
||||
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
|
||||
|
||||
x = self.norm(x)
|
||||
x = self.reduction(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class BasicLayer(nn.Module):
|
||||
""" A basic Swin Transformer layer for one stage.
|
||||
|
||||
Args:
|
||||
dim (int): Number of feature channels
|
||||
depth (int): Depths of this stage.
|
||||
num_heads (int): Number of attention head.
|
||||
window_size (int): Local window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop (float, optional): Dropout rate. Default: 0.0
|
||||
attn_drop (float, optional): Attention dropout rate. Default: 0.0
|
||||
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
|
||||
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
dim,
|
||||
depth,
|
||||
num_heads,
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop=0.,
|
||||
attn_drop=0.,
|
||||
drop_path=0.,
|
||||
norm_layer=nn.LayerNorm,
|
||||
downsample=None,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
self.window_size = window_size
|
||||
self.shift_size = window_size // 2
|
||||
self.depth = depth
|
||||
self.use_checkpoint = use_checkpoint
|
||||
|
||||
# build blocks
|
||||
self.blocks = nn.ModuleList([
|
||||
SwinTransformerBlock(
|
||||
dim=dim,
|
||||
num_heads=num_heads,
|
||||
window_size=window_size,
|
||||
shift_size=0 if (i % 2 == 0) else window_size // 2,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop,
|
||||
attn_drop=attn_drop,
|
||||
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
|
||||
norm_layer=norm_layer)
|
||||
for i in range(depth)])
|
||||
|
||||
# patch merging layer
|
||||
if downsample is not None:
|
||||
self.downsample = downsample(dim=dim, norm_layer=norm_layer)
|
||||
else:
|
||||
self.downsample = None
|
||||
|
||||
def forward(self, x, H, W):
|
||||
""" Forward function.
|
||||
|
||||
Args:
|
||||
x: Input feature, tensor size (B, H*W, C).
|
||||
H, W: Spatial resolution of the input feature.
|
||||
"""
|
||||
|
||||
# calculate attention mask for SW-MSA
|
||||
Hp = int(np.ceil(H / self.window_size)) * self.window_size
|
||||
Wp = int(np.ceil(W / self.window_size)) * self.window_size
|
||||
img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
|
||||
h_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
w_slices = (slice(0, -self.window_size),
|
||||
slice(-self.window_size, -self.shift_size),
|
||||
slice(-self.shift_size, None))
|
||||
cnt = 0
|
||||
for h in h_slices:
|
||||
for w in w_slices:
|
||||
img_mask[:, h, w, :] = cnt
|
||||
cnt += 1
|
||||
|
||||
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
|
||||
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
|
||||
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
|
||||
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
|
||||
|
||||
for blk in self.blocks:
|
||||
blk.H, blk.W = H, W
|
||||
if self.use_checkpoint:
|
||||
x = checkpoint.checkpoint(blk, x, attn_mask)
|
||||
else:
|
||||
x = blk(x, attn_mask)
|
||||
if self.downsample is not None:
|
||||
x_down = self.downsample(x, H, W)
|
||||
Wh, Ww = (H + 1) // 2, (W + 1) // 2
|
||||
return x, H, W, x_down, Wh, Ww
|
||||
else:
|
||||
return x, H, W, x, H, W
|
||||
|
||||
|
||||
class PatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
|
||||
Args:
|
||||
patch_size (int): Patch token size. Default: 4.
|
||||
in_channels (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
norm_layer (nn.Module, optional): Normalization layer. Default: None
|
||||
"""
|
||||
|
||||
def __init__(self, patch_size=4, in_channels=3, embed_dim=96, norm_layer=None):
|
||||
super().__init__()
|
||||
patch_size = to_2tuple(patch_size)
|
||||
self.patch_size = patch_size
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=patch_size)
|
||||
if norm_layer is not None:
|
||||
self.norm = norm_layer(embed_dim)
|
||||
else:
|
||||
self.norm = None
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
# padding
|
||||
_, _, H, W = x.size()
|
||||
if W % self.patch_size[1] != 0:
|
||||
x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
|
||||
if H % self.patch_size[0] != 0:
|
||||
x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
|
||||
|
||||
x = self.proj(x) # B C Wh Ww
|
||||
if self.norm is not None:
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SwinTransformer(nn.Module):
|
||||
""" Swin Transformer backbone.
|
||||
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
|
||||
https://arxiv.org/pdf/2103.14030
|
||||
|
||||
Args:
|
||||
pretrain_img_size (int): Input image size for training the pretrained model,
|
||||
used in absolute postion embedding. Default 224.
|
||||
patch_size (int | tuple(int)): Patch size. Default: 4.
|
||||
in_channels (int): Number of input image channels. Default: 3.
|
||||
embed_dim (int): Number of linear projection output channels. Default: 96.
|
||||
depths (tuple[int]): Depths of each Swin Transformer stage.
|
||||
num_heads (tuple[int]): Number of attention head of each stage.
|
||||
window_size (int): Window size. Default: 7.
|
||||
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
|
||||
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
|
||||
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
|
||||
drop_rate (float): Dropout rate.
|
||||
attn_drop_rate (float): Attention dropout rate. Default: 0.
|
||||
drop_path_rate (float): Stochastic depth rate. Default: 0.2.
|
||||
norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
|
||||
ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
|
||||
patch_norm (bool): If True, add normalization after patch embedding. Default: True.
|
||||
out_indices (Sequence[int]): Output from which stages.
|
||||
frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
|
||||
-1 means not freezing any parameters.
|
||||
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
pretrain_img_size=224,
|
||||
patch_size=4,
|
||||
in_channels=3,
|
||||
embed_dim=96,
|
||||
depths=[2, 2, 6, 2],
|
||||
num_heads=[3, 6, 12, 24],
|
||||
window_size=7,
|
||||
mlp_ratio=4.,
|
||||
qkv_bias=True,
|
||||
qk_scale=None,
|
||||
drop_rate=0.,
|
||||
attn_drop_rate=0.,
|
||||
drop_path_rate=0.2,
|
||||
norm_layer=nn.LayerNorm,
|
||||
ape=False,
|
||||
patch_norm=True,
|
||||
out_indices=(0, 1, 2, 3),
|
||||
frozen_stages=-1,
|
||||
use_checkpoint=False):
|
||||
super().__init__()
|
||||
|
||||
self.pretrain_img_size = pretrain_img_size
|
||||
self.num_layers = len(depths)
|
||||
self.embed_dim = embed_dim
|
||||
self.ape = ape
|
||||
self.patch_norm = patch_norm
|
||||
self.out_indices = out_indices
|
||||
self.frozen_stages = frozen_stages
|
||||
|
||||
# split image into non-overlapping patches
|
||||
self.patch_embed = PatchEmbed(
|
||||
patch_size=patch_size, in_channels=in_channels, embed_dim=embed_dim,
|
||||
norm_layer=norm_layer if self.patch_norm else None)
|
||||
|
||||
# absolute position embedding
|
||||
if self.ape:
|
||||
pretrain_img_size = to_2tuple(pretrain_img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
patches_resolution = [pretrain_img_size[0] // patch_size[0], pretrain_img_size[1] // patch_size[1]]
|
||||
|
||||
self.absolute_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1]))
|
||||
trunc_normal_(self.absolute_pos_embed, std=.02)
|
||||
|
||||
self.pos_drop = nn.Dropout(p=drop_rate)
|
||||
|
||||
# stochastic depth
|
||||
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
|
||||
|
||||
# build layers
|
||||
self.layers = nn.ModuleList()
|
||||
for i_layer in range(self.num_layers):
|
||||
layer = BasicLayer(
|
||||
dim=int(embed_dim * 2 ** i_layer),
|
||||
depth=depths[i_layer],
|
||||
num_heads=num_heads[i_layer],
|
||||
window_size=window_size,
|
||||
mlp_ratio=mlp_ratio,
|
||||
qkv_bias=qkv_bias,
|
||||
qk_scale=qk_scale,
|
||||
drop=drop_rate,
|
||||
attn_drop=attn_drop_rate,
|
||||
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
|
||||
norm_layer=norm_layer,
|
||||
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
|
||||
use_checkpoint=use_checkpoint)
|
||||
self.layers.append(layer)
|
||||
|
||||
num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
|
||||
self.num_features = num_features
|
||||
|
||||
# add a norm layer for each output
|
||||
for i_layer in out_indices:
|
||||
layer = norm_layer(num_features[i_layer])
|
||||
layer_name = f'norm{i_layer}'
|
||||
self.add_module(layer_name, layer)
|
||||
|
||||
self._freeze_stages()
|
||||
|
||||
def _freeze_stages(self):
|
||||
if self.frozen_stages >= 0:
|
||||
self.patch_embed.eval()
|
||||
for param in self.patch_embed.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 1 and self.ape:
|
||||
self.absolute_pos_embed.requires_grad = False
|
||||
|
||||
if self.frozen_stages >= 2:
|
||||
self.pos_drop.eval()
|
||||
for i in range(0, self.frozen_stages - 1):
|
||||
m = self.layers[i]
|
||||
m.eval()
|
||||
for param in m.parameters():
|
||||
param.requires_grad = False
|
||||
|
||||
def init_weights(self, pretrained=None):
|
||||
"""Initialize the weights in backbone.
|
||||
|
||||
Args:
|
||||
pretrained (str, optional): Path to pre-trained weights.
|
||||
Defaults to None.
|
||||
"""
|
||||
|
||||
def _init_weights(m):
|
||||
if isinstance(m, nn.Linear):
|
||||
trunc_normal_(m.weight, std=.02)
|
||||
if isinstance(m, nn.Linear) and m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
nn.init.constant_(m.bias, 0)
|
||||
nn.init.constant_(m.weight, 1.0)
|
||||
|
||||
if isinstance(pretrained, str):
|
||||
self.apply(_init_weights)
|
||||
logger = get_root_logger()
|
||||
load_checkpoint(self, pretrained, strict=False, logger=logger)
|
||||
elif pretrained is None:
|
||||
self.apply(_init_weights)
|
||||
else:
|
||||
raise TypeError('pretrained must be a str or None')
|
||||
|
||||
def forward(self, x):
|
||||
"""Forward function."""
|
||||
x = self.patch_embed(x)
|
||||
|
||||
Wh, Ww = x.size(2), x.size(3)
|
||||
if self.ape:
|
||||
# interpolate the position embedding to the corresponding size
|
||||
absolute_pos_embed = F.interpolate(self.absolute_pos_embed, size=(Wh, Ww), mode='bicubic')
|
||||
x = (x + absolute_pos_embed) # B Wh*Ww C
|
||||
|
||||
outs = []#x.contiguous()]
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.pos_drop(x)
|
||||
for i in range(self.num_layers):
|
||||
layer = self.layers[i]
|
||||
x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
|
||||
|
||||
if i in self.out_indices:
|
||||
norm_layer = getattr(self, f'norm{i}')
|
||||
x_out = norm_layer(x_out)
|
||||
|
||||
out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
|
||||
outs.append(out)
|
||||
|
||||
return tuple(outs)
|
||||
|
||||
def train(self, mode=True):
|
||||
"""Convert the model into training mode while keep layers freezed."""
|
||||
super(SwinTransformer, self).train(mode)
|
||||
self._freeze_stages()
|
||||
|
||||
def swin_v1_t():
|
||||
model = SwinTransformer(embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7)
|
||||
return model
|
||||
|
||||
def swin_v1_s():
|
||||
model = SwinTransformer(embed_dim=96, depths=[2, 2, 18, 2], num_heads=[3, 6, 12, 24], window_size=7)
|
||||
return model
|
||||
|
||||
def swin_v1_b():
|
||||
model = SwinTransformer(embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12)
|
||||
return model
|
||||
|
||||
def swin_v1_l():
|
||||
model = SwinTransformer(embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12)
|
||||
return model
|
||||
@@ -0,0 +1,292 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from collections import OrderedDict
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torchvision.models import vgg16, vgg16_bn
|
||||
from torchvision.models import resnet50
|
||||
from kornia.filters import laplacian
|
||||
|
||||
from BiRefNet.backbones.build_backbone import build_backbone
|
||||
from BiRefNet.modules.decoder_blocks import BasicDecBlk, ResBlk, HierarAttDecBlk
|
||||
from BiRefNet.modules.lateral_blocks import BasicLatBlk
|
||||
from BiRefNet.modules.aspp import ASPP, ASPPDeformable
|
||||
from BiRefNet.modules.ing import *
|
||||
from BiRefNet.refinement.refiner import Refiner, RefinerPVTInChannels4, RefUNet
|
||||
from BiRefNet.refinement.stem_layer import StemLayer
|
||||
|
||||
from .config import Config
|
||||
from .dataset import class_labels_TR_sorted
|
||||
|
||||
|
||||
class BiRefNet(nn.Module):
|
||||
def __init__(self):
|
||||
super(BiRefNet, self).__init__()
|
||||
self.config = Config()
|
||||
self.epoch = 1
|
||||
self.bb = build_backbone(self.config.bb, pretrained=True)
|
||||
|
||||
channels = self.config.lateral_channels_in_collection
|
||||
|
||||
if self.config.auxiliary_classification:
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
||||
self.cls_head = nn.Sequential(
|
||||
nn.Linear(channels[0], len(class_labels_TR_sorted))
|
||||
)
|
||||
|
||||
if self.config.squeeze_block:
|
||||
self.squeeze_module = nn.Sequential(*[
|
||||
eval(self.config.squeeze_block.split('_x')[0])(channels[0]+sum(self.config.cxt), channels[0])
|
||||
for _ in range(eval(self.config.squeeze_block.split('_x')[1]))
|
||||
])
|
||||
|
||||
self.decoder = Decoder(channels)
|
||||
|
||||
if self.config.locate_head:
|
||||
self.locate_header = nn.ModuleList([
|
||||
BasicDecBlk(channels[0], channels[-1]),
|
||||
nn.Sequential(
|
||||
nn.Conv2d(channels[-1], 1, 1, 1, 0),
|
||||
)
|
||||
])
|
||||
|
||||
if self.config.ender:
|
||||
self.dec_end = nn.Sequential(
|
||||
nn.Conv2d(1, 16, 3, 1, 1),
|
||||
nn.Conv2d(16, 1, 3, 1, 1),
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
|
||||
# refine patch-level segmentation
|
||||
if self.config.refine:
|
||||
if self.config.refine == 'itself':
|
||||
self.stem_layer = StemLayer(in_channels=3+1, inter_channels=48, out_channels=3)
|
||||
else:
|
||||
self.refiner = eval('{}({})'.format(self.config.refine, 'in_channels=3+1'))
|
||||
|
||||
if self.config.freeze_bb:
|
||||
# Freeze the backbone...
|
||||
print(self.named_parameters())
|
||||
for key, value in self.named_parameters():
|
||||
if 'bb.' in key and 'refiner.' not in key:
|
||||
value.requires_grad = False
|
||||
|
||||
def forward_enc(self, x):
|
||||
if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
|
||||
x1 = self.bb.conv1(x); x2 = self.bb.conv2(x1); x3 = self.bb.conv3(x2); x4 = self.bb.conv4(x3)
|
||||
else:
|
||||
x1, x2, x3, x4 = self.bb(x)
|
||||
if self.config.mul_scl_ipt == 'cat':
|
||||
B, C, H, W = x.shape
|
||||
x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
|
||||
x1 = torch.cat([x1, F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x2 = torch.cat([x2, F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x3 = torch.cat([x3, F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
x4 = torch.cat([x4, F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)], dim=1)
|
||||
elif self.config.mul_scl_ipt == 'add':
|
||||
B, C, H, W = x.shape
|
||||
x1_, x2_, x3_, x4_ = self.bb(F.interpolate(x, size=(H//2, W//2), mode='bilinear', align_corners=True))
|
||||
x1 = x1 + F.interpolate(x1_, size=x1.shape[2:], mode='bilinear', align_corners=True)
|
||||
x2 = x2 + F.interpolate(x2_, size=x2.shape[2:], mode='bilinear', align_corners=True)
|
||||
x3 = x3 + F.interpolate(x3_, size=x3.shape[2:], mode='bilinear', align_corners=True)
|
||||
x4 = x4 + F.interpolate(x4_, size=x4.shape[2:], mode='bilinear', align_corners=True)
|
||||
class_preds = self.cls_head(self.avgpool(x4).view(x4.shape[0], -1)) if self.training and self.config.auxiliary_classification else None
|
||||
if self.config.cxt:
|
||||
x4 = torch.cat(
|
||||
(
|
||||
*[
|
||||
F.interpolate(x1, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
F.interpolate(x2, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
F.interpolate(x3, size=x4.shape[2:], mode='bilinear', align_corners=True),
|
||||
][-len(self.config.cxt):],
|
||||
x4
|
||||
),
|
||||
dim=1
|
||||
)
|
||||
return (x1, x2, x3, x4), class_preds
|
||||
|
||||
def forward_ori(self, x):
|
||||
########## Encoder ##########
|
||||
(x1, x2, x3, x4), class_preds = self.forward_enc(x)
|
||||
if self.config.squeeze_block:
|
||||
x4 = self.squeeze_module(x4)
|
||||
########## Decoder ##########
|
||||
features = [x, x1, x2, x3, x4]
|
||||
if self.config.out_ref:
|
||||
features.append(laplacian(torch.mean(x, dim=1).unsqueeze(1), kernel_size=5))
|
||||
scaled_preds = self.decoder(features)
|
||||
return scaled_preds, class_preds
|
||||
|
||||
def forward_ref(self, x, pred):
|
||||
# refine patch-level segmentation
|
||||
if pred.shape[2:] != x.shape[2:]:
|
||||
pred = F.interpolate(pred, size=x.shape[2:], mode='bilinear', align_corners=True)
|
||||
# pred = pred.sigmoid()
|
||||
if self.config.refine == 'itself':
|
||||
x = self.stem_layer(torch.cat([x, pred], dim=1))
|
||||
scaled_preds, class_preds = self.forward_ori(x)
|
||||
else:
|
||||
scaled_preds = self.refiner([x, pred])
|
||||
class_preds = None
|
||||
return scaled_preds, class_preds
|
||||
|
||||
def forward_ref_end(self, x):
|
||||
# remove the grids of concatenated preds
|
||||
return self.dec_end(x) if self.config.ender else x
|
||||
|
||||
|
||||
def forward(self, x):
|
||||
scaled_preds, class_preds = self.forward_ori(x)
|
||||
class_preds_lst = [class_preds]
|
||||
return [scaled_preds, class_preds_lst] if self.training else scaled_preds
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super(Decoder, self).__init__()
|
||||
self.config = Config()
|
||||
DecoderBlock = eval(self.config.dec_blk)
|
||||
LateralBlock = eval(self.config.lat_blk)
|
||||
|
||||
if self.config.dec_ipt:
|
||||
self.split = self.config.dec_ipt_split
|
||||
N_dec_ipt = 64
|
||||
DBlock = SimpleConvs
|
||||
ic = 64
|
||||
ipt_cha_opt = 1
|
||||
self.ipt_blk4 = DBlock(2**8*3 if self.split else 3, [N_dec_ipt, channels[0]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk3 = DBlock(2**6*3 if self.split else 3, [N_dec_ipt, channels[1]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk2 = DBlock(2**4*3 if self.split else 3, [N_dec_ipt, channels[2]//8][ipt_cha_opt], inter_channels=ic)
|
||||
self.ipt_blk1 = DBlock(2**0*3 if self.split else 3, [N_dec_ipt, channels[3]//8][ipt_cha_opt], inter_channels=ic)
|
||||
else:
|
||||
self.split = None
|
||||
|
||||
self.decoder_block4 = DecoderBlock(channels[0], channels[1])
|
||||
self.decoder_block3 = DecoderBlock(channels[1]+([N_dec_ipt, channels[0]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[2])
|
||||
self.decoder_block2 = DecoderBlock(channels[2]+([N_dec_ipt, channels[1]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3])
|
||||
self.decoder_block1 = DecoderBlock(channels[3]+([N_dec_ipt, channels[2]//8][ipt_cha_opt] if self.config.dec_ipt else 0), channels[3]//2)
|
||||
self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2+([N_dec_ipt, channels[3]//8][ipt_cha_opt] if self.config.dec_ipt else 0), 1, 1, 1, 0))
|
||||
|
||||
self.lateral_block4 = LateralBlock(channels[1], channels[1])
|
||||
self.lateral_block3 = LateralBlock(channels[2], channels[2])
|
||||
self.lateral_block2 = LateralBlock(channels[3], channels[3])
|
||||
|
||||
if self.config.ms_supervision:
|
||||
self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
|
||||
|
||||
if self.config.out_ref:
|
||||
_N = 16
|
||||
# self.gdt_convs_4 = nn.Sequential(nn.Conv2d(channels[1], _N, 3, 1, 1), nn.BatchNorm2d(_N), nn.ReLU(inplace=True))
|
||||
self.gdt_convs_3 = nn.Sequential(nn.Conv2d(channels[2], _N, 3, 1, 1), nn.BatchNorm2d(_N), nn.ReLU(inplace=True))
|
||||
self.gdt_convs_2 = nn.Sequential(nn.Conv2d(channels[3], _N, 3, 1, 1), nn.BatchNorm2d(_N), nn.ReLU(inplace=True))
|
||||
|
||||
# self.gdt_convs_pred_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_pred_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_pred_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
|
||||
# self.gdt_convs_attn_4 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_attn_3 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
self.gdt_convs_attn_2 = nn.Sequential(nn.Conv2d(_N, 1, 1, 1, 0))
|
||||
|
||||
|
||||
def get_patches_batch(self, x, p):
|
||||
_size_h, _size_w = p.shape[2:]
|
||||
patches_batch = []
|
||||
for idx in range(x.shape[0]):
|
||||
columns_x = torch.split(x[idx], split_size_or_sections=_size_w, dim=-1)
|
||||
patches_x = []
|
||||
for column_x in columns_x:
|
||||
patches_x += [p.unsqueeze(0) for p in torch.split(column_x, split_size_or_sections=_size_h, dim=-2)]
|
||||
patch_sample = torch.cat(patches_x, dim=1)
|
||||
patches_batch.append(patch_sample)
|
||||
return torch.cat(patches_batch, dim=0)
|
||||
|
||||
def forward(self, features):
|
||||
if self.config.out_ref:
|
||||
outs_gdt_pred = []
|
||||
outs_gdt_label = []
|
||||
x, x1, x2, x3, x4, gdt_gt = features
|
||||
else:
|
||||
x, x1, x2, x3, x4 = features
|
||||
outs = []
|
||||
p4 = self.decoder_block4(x4)
|
||||
m4 = self.conv_ms_spvn_4(p4) if self.config.ms_supervision else None
|
||||
_p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p3 = _p4 + self.lateral_block4(x3)
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p3) if self.split else x
|
||||
_p3 = torch.cat((_p3, self.ipt_blk4(F.interpolate(patches_batch, size=x3.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
|
||||
p3 = self.decoder_block3(_p3)
|
||||
m3 = self.conv_ms_spvn_3(p3) if self.config.ms_supervision else None
|
||||
if self.config.out_ref:
|
||||
# >> GT:
|
||||
# m3 --dilation--> m3_dia
|
||||
# G_3^gt * m3_dia --> G_3^m, which is the label of gradient
|
||||
m3_dia = m3
|
||||
gdt_label_main_3 = gdt_gt * F.interpolate(m3_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
|
||||
outs_gdt_label.append(gdt_label_main_3)
|
||||
# >> Pred:
|
||||
# p3 --conv--BN--> F_3^G, where F_3^G predicts the \hat{G_3} with xx
|
||||
# F_3^G --sigmoid--> A_3^G
|
||||
p3_gdt = self.gdt_convs_3(p3)
|
||||
gdt_pred_3 = self.gdt_convs_pred_3(p3_gdt)
|
||||
outs_gdt_pred.append(gdt_pred_3)
|
||||
gdt_attn_3 = self.gdt_convs_attn_3(p3_gdt).sigmoid()
|
||||
# >> Finally:
|
||||
# p3 = p3 * A_3^G
|
||||
p3 = p3 * gdt_attn_3
|
||||
_p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p2 = _p3 + self.lateral_block3(x2)
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p2) if self.split else x
|
||||
_p2 = torch.cat((_p2, self.ipt_blk3(F.interpolate(patches_batch, size=x2.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
|
||||
p2 = self.decoder_block2(_p2)
|
||||
m2 = self.conv_ms_spvn_2(p2) if self.config.ms_supervision else None
|
||||
if self.config.out_ref:
|
||||
# >> GT:
|
||||
m2_dia = m2
|
||||
gdt_label_main_2 = gdt_gt * F.interpolate(m2_dia, size=gdt_gt.shape[2:], mode='bilinear', align_corners=True)
|
||||
outs_gdt_label.append(gdt_label_main_2)
|
||||
# >> Pred:
|
||||
p2_gdt = self.gdt_convs_2(p2)
|
||||
gdt_pred_2 = self.gdt_convs_pred_2(p2_gdt)
|
||||
outs_gdt_pred.append(gdt_pred_2)
|
||||
gdt_attn_2 = self.gdt_convs_attn_2(p2_gdt).sigmoid()
|
||||
# >> Finally:
|
||||
p2 = p2 * gdt_attn_2
|
||||
_p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p1 = _p2 + self.lateral_block2(x1)
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p1) if self.split else x
|
||||
_p1 = torch.cat((_p1, self.ipt_blk2(F.interpolate(patches_batch, size=x1.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
|
||||
_p1 = self.decoder_block1(_p1)
|
||||
_p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
|
||||
if self.config.dec_ipt:
|
||||
patches_batch = self.get_patches_batch(x, _p1) if self.split else x
|
||||
_p1 = torch.cat((_p1, self.ipt_blk1(F.interpolate(patches_batch, size=x.shape[2:], mode='bilinear', align_corners=True))), 1)
|
||||
p1_out = self.conv_out1(_p1)
|
||||
|
||||
if self.config.ms_supervision:
|
||||
outs.append(m4)
|
||||
outs.append(m3)
|
||||
outs.append(m2)
|
||||
outs.append(p1_out)
|
||||
return outs if not (self.config.out_ref and self.training) else ([outs_gdt_pred, outs_gdt_label], outs)
|
||||
|
||||
|
||||
class SimpleConvs(nn.Module):
|
||||
def __init__(
|
||||
self, in_channels: int, out_channels: int, inter_channels=64
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
|
||||
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv_out(self.conv1(x))
|
||||
@@ -0,0 +1,104 @@
|
||||
import os
|
||||
import math
|
||||
from folder_paths import models_dir
|
||||
|
||||
|
||||
class Config():
|
||||
def __init__(self) -> None:
|
||||
self.ms_supervision = True
|
||||
self.out_ref = self.ms_supervision and True
|
||||
self.dec_ipt = True
|
||||
self.dec_ipt_split = True
|
||||
self.locate_head = False
|
||||
self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder
|
||||
self.mul_scl_ipt = ['', 'add', 'cat'][2]
|
||||
self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0]
|
||||
self.progressive_ref = self.refine and True
|
||||
self.ender = self.progressive_ref and False
|
||||
self.scale = self.progressive_ref and 2
|
||||
self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2]
|
||||
self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1]
|
||||
self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0]
|
||||
self.auxiliary_classification = False
|
||||
self.refine_iteration = 1
|
||||
self.freeze_bb = False
|
||||
self.precisionHigh = True
|
||||
self.compile = True
|
||||
self.load_all = True
|
||||
self.verbose_eval = True
|
||||
|
||||
self.size = 1024
|
||||
self.batch_size = 2
|
||||
self.IoU_finetune_last_epochs = [0, -40][1] # choose 0 to skip
|
||||
if self.dec_blk == 'HierarAttDecBlk':
|
||||
self.batch_size = 2 ** [0, 1, 2, 3, 4][2]
|
||||
self.model = [
|
||||
'BiRefNet',
|
||||
][0]
|
||||
|
||||
# Components
|
||||
self.lat_blk = ['BasicLatBlk'][0]
|
||||
self.dec_channels_inter = ['fixed', 'adap'][0]
|
||||
|
||||
# Backbone
|
||||
self.bb = [
|
||||
'vgg16', 'vgg16bn', 'resnet50', # 0, 1, 2
|
||||
'pvt_v2_b2', 'pvt_v2_b5', # 3-bs10, 4-bs5
|
||||
'swin_v1_b', 'swin_v1_l' # 5-bs9, 6-bs6
|
||||
][6]
|
||||
self.lateral_channels_in_collection = {
|
||||
'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
|
||||
'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
|
||||
'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
|
||||
}[self.bb]
|
||||
if self.mul_scl_ipt == 'cat':
|
||||
self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection]
|
||||
self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else []
|
||||
self.sys_home_dir = models_dir
|
||||
self.weights_root_dir = os.path.join(self.sys_home_dir, "BiRefNet")
|
||||
self.weights = {
|
||||
'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'),
|
||||
'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]),
|
||||
'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]),
|
||||
'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]),
|
||||
}
|
||||
|
||||
# Training
|
||||
self.num_workers = 5 # will be decrease to min(it, batch_size) at the initialization of the data_loader
|
||||
self.optimizer = ['Adam', 'AdamW'][0]
|
||||
self.lr = 1e-5 * math.sqrt(self.batch_size / 5) # adapt the lr linearly
|
||||
self.lr_decay_epochs = [1e4] # Set to negative N to decay the lr in the last N-th epoch.
|
||||
self.lr_decay_rate = 0.5
|
||||
self.only_S_MAE = False
|
||||
self.SDPA_enabled = False # Bug. Slower and errors occur in multi-GPUs
|
||||
|
||||
# Data
|
||||
self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis')
|
||||
self.dataset = ['DIS5K', 'COD', 'SOD'][0]
|
||||
self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4]
|
||||
|
||||
# Loss
|
||||
self.lambdas_pix_last = {
|
||||
# not 0 means opening this loss
|
||||
# original rate -- 1 : 30 : 1.5 : 0.2, bce x 30
|
||||
'bce': 30 * 1, # high performance
|
||||
'iou': 0.5 * 1, # 0 / 255
|
||||
'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64)
|
||||
'mse': 150 * 0, # can smooth the saliency map
|
||||
'triplet': 3 * 0,
|
||||
'reg': 100 * 0,
|
||||
'ssim': 10 * 1, # help contours,
|
||||
'cnt': 5 * 0, # help contours
|
||||
}
|
||||
self.lambdas_cls = {
|
||||
'ce': 5.0
|
||||
}
|
||||
# Adv
|
||||
self.lambda_adv_g = 10. * 0 # turn to 0 to avoid adv training
|
||||
self.lambda_adv_d = 3. * (self.lambda_adv_g > 0)
|
||||
|
||||
# others
|
||||
self.device = [0, 'cpu'][0] # .to(0) = .to('cuda:0')
|
||||
|
||||
self.batch_size_valid = 1
|
||||
self.rand_seed = 7
|
||||
@@ -0,0 +1,140 @@
|
||||
import os
|
||||
import cv2
|
||||
from tqdm import tqdm
|
||||
from PIL import Image
|
||||
from torch.utils import data
|
||||
from torchvision import transforms
|
||||
|
||||
from .preproc import preproc
|
||||
from .config import Config
|
||||
from glob import glob
|
||||
|
||||
|
||||
Image.MAX_IMAGE_PIXELS = None # remove DecompressionBombWarning
|
||||
config = Config()
|
||||
_class_labels_TR_sorted = 'Airplane, Ant, Antenna, Archery, Axe, BabyCarriage, Bag, BalanceBeam, Balcony, Balloon, Basket, BasketballHoop, Beatle, Bed, Bee, Bench, Bicycle, BicycleFrame, BicycleStand, Boat, Bonsai, BoomLift, Bridge, BunkBed, Butterfly, Button, Cable, CableLift, Cage, Camcorder, Cannon, Canoe, Car, CarParkDropArm, Carriage, Cart, Caterpillar, CeilingLamp, Centipede, Chair, Clip, Clock, Clothes, CoatHanger, Comb, ConcretePumpTruck, Crack, Crane, Cup, DentalChair, Desk, DeskChair, Diagram, DishRack, DoorHandle, Dragonfish, Dragonfly, Drum, Earphone, Easel, ElectricIron, Excavator, Eyeglasses, Fan, Fence, Fencing, FerrisWheel, FireExtinguisher, Fishing, Flag, FloorLamp, Forklift, GasStation, Gate, Gear, Goal, Golf, GymEquipment, Hammock, Handcart, Handcraft, Handrail, HangGlider, Harp, Harvester, Headset, Helicopter, Helmet, Hook, HorizontalBar, Hydrovalve, IroningTable, Jewelry, Key, KidsPlayground, Kitchenware, Kite, Knife, Ladder, LaundryRack, Lightning, Lobster, Locust, Machine, MachineGun, MagazineRack, Mantis, Medal, MemorialArchway, Microphone, Missile, MobileHolder, Monitor, Mosquito, Motorcycle, MovingTrolley, Mower, MusicPlayer, MusicStand, ObservationTower, Octopus, OilWell, OlympicLogo, OperatingTable, OutdoorFitnessEquipment, Parachute, Pavilion, Piano, Pipe, PlowHarrow, PoleVault, Punchbag, Rack, Racket, Rifle, Ring, Robot, RockClimbing, Rope, Sailboat, Satellite, Scaffold, Scale, Scissor, Scooter, Sculpture, Seadragon, Seahorse, Seal, SewingMachine, Ship, Shoe, ShoppingCart, ShoppingTrolley, Shower, Shrimp, Signboard, Skateboarding, Skeleton, Skiing, Spade, SpeedBoat, Spider, Spoon, Stair, Stand, Stationary, SteeringWheel, Stethoscope, Stool, Stove, StreetLamp, SweetStand, Swing, Sword, TV, Table, TableChair, TableLamp, TableTennis, Tank, Tapeline, Teapot, Telescope, Tent, TobaccoPipe, Toy, Tractor, TrafficLight, TrafficSign, Trampoline, TransmissionTower, Tree, Tricycle, TrimmerCover, Tripod, Trombone, Truck, Trumpet, Tuba, UAV, Umbrella, UnevenBars, UtilityPole, VacuumCleaner, Violin, Wakesurfing, Watch, WaterTower, WateringPot, Well, WellLid, Wheel, Wheelchair, WindTurbine, Windmill, WineGlass, WireWhisk, Yacht'
|
||||
class_labels_TR_sorted = _class_labels_TR_sorted.split(', ')
|
||||
|
||||
|
||||
class MyData(data.Dataset):
|
||||
def __init__(self, data_root, image_size, is_train=True):
|
||||
self.size_train = image_size
|
||||
self.size_test = image_size
|
||||
self.keep_size = not config.size
|
||||
self.data_size = (config.size, config.size)
|
||||
self.is_train = is_train
|
||||
self.load_all = config.load_all
|
||||
self.device = config.device
|
||||
self.dataset = data_root.replace('\\', '/').split('/')[-1]
|
||||
if self.is_train and config.auxiliary_classification:
|
||||
self.cls_name2id = {_name: _id for _id, _name in enumerate(class_labels_TR_sorted)}
|
||||
self.transform_image = transforms.Compose([
|
||||
transforms.Resize(self.data_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
||||
][self.load_all or self.keep_size:])
|
||||
self.transform_label = transforms.Compose([
|
||||
transforms.Resize(self.data_size),
|
||||
transforms.ToTensor(),
|
||||
][self.load_all or self.keep_size:])
|
||||
## 'im' and 'gt' need modifying
|
||||
image_root = os.path.join(data_root, 'im')
|
||||
self.image_paths = [os.path.join(image_root, p) for p in os.listdir(image_root)]
|
||||
self.label_paths = [p.replace('/im/', '/gt/').replace('.jpg', '.png') for p in self.image_paths]
|
||||
if self.load_all:
|
||||
self.images_loaded, self.labels_loaded = [], []
|
||||
self.class_labels_loaded = []
|
||||
# for image_path, label_path in zip(self.image_paths, self.label_paths):
|
||||
for image_path, label_path in tqdm(zip(self.image_paths, self.label_paths), total=len(self.image_paths)):
|
||||
_image = cv2.imread(image_path)
|
||||
_label = cv2.imread(label_path, cv2.IMREAD_GRAYSCALE)
|
||||
if not self.keep_size:
|
||||
_image_rs = cv2.resize(_image, (config.size, config.size), interpolation=cv2.INTER_LINEAR)
|
||||
_label_rs = cv2.resize(_label, (config.size, config.size), interpolation=cv2.INTER_LINEAR)
|
||||
self.images_loaded.append(
|
||||
Image.fromarray(cv2.cvtColor(_image_rs, cv2.COLOR_BGR2RGB)).convert('RGB')
|
||||
)
|
||||
self.labels_loaded.append(
|
||||
Image.fromarray(_label_rs).convert('L')
|
||||
)
|
||||
self.class_labels_loaded.append(
|
||||
self.cls_name2id[label_path.split('/')[-1].split('#')[3]] if self.is_train and config.auxiliary_classification else -1
|
||||
)
|
||||
|
||||
|
||||
def __getitem__(self, index):
|
||||
|
||||
if self.load_all:
|
||||
image = self.images_loaded[index]
|
||||
class_label = self.class_labels_loaded[index] if self.is_train and config.auxiliary_classification else -1
|
||||
else:
|
||||
image = Image.open(self.image_paths[index]).convert('RGB')
|
||||
|
||||
# loading image and label
|
||||
if self.is_train:
|
||||
image, label = preproc(image, image, preproc_methods=config.preproc_methods)
|
||||
# else:
|
||||
# if _label.shape[0] > 2048 or _label.shape[1] > 2048:
|
||||
# _image = cv2.resize(_image, (2048, 2048), interpolation=cv2.INTER_LINEAR)
|
||||
# _label = cv2.resize(_label, (2048, 2048), interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
image, label = self.transform_image(image), self.transform_label(label)
|
||||
|
||||
if self.is_train:
|
||||
return image, label, class_label
|
||||
else:
|
||||
return image, label, self.label_paths[index]
|
||||
|
||||
def __len__(self):
|
||||
return len(self.image_paths)
|
||||
|
||||
|
||||
class YouData(data.Dataset):
|
||||
def __init__(self, data_root, image_size, is_train=True):
|
||||
self.size_train = image_size
|
||||
self.size_test = image_size
|
||||
self.keep_size = not config.size
|
||||
self.data_size = (config.size, config.size)
|
||||
self.is_train = is_train
|
||||
self.load_all = config.load_all
|
||||
self.device = config.device
|
||||
self.dataset = data_root.replace('\\', '/').split('/')[-1]
|
||||
if self.is_train and config.auxiliary_classification:
|
||||
self.cls_name2id = {_name: _id for _id, _name in enumerate(class_labels_TR_sorted)}
|
||||
self.transform_image = transforms.Compose([
|
||||
transforms.Resize(self.data_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
||||
][self.load_all or self.keep_size:])
|
||||
## 'im' and 'gt' need modifying
|
||||
self.image_paths = glob(data_root + "/*")
|
||||
self.img_sizes = []
|
||||
if self.load_all:
|
||||
self.images_loaded, self.labels_loaded = [], []
|
||||
for image_path in tqdm(self.image_paths, total=len(self.image_paths)):
|
||||
_image = cv2.imread(image_path)
|
||||
if not self.keep_size:
|
||||
_image_rs = cv2.resize(_image, (config.size, config.size), interpolation=cv2.INTER_LINEAR)
|
||||
self.images_loaded.append(
|
||||
Image.fromarray(cv2.cvtColor(_image_rs, cv2.COLOR_BGR2RGB)).convert('RGB')
|
||||
)
|
||||
self.img_sizes.append(_image.shape[:2])
|
||||
|
||||
|
||||
def __getitem__(self, index):
|
||||
|
||||
if self.load_all:
|
||||
image = self.images_loaded[index]
|
||||
else:
|
||||
image = Image.open(self.image_paths[index]).convert('RGB')
|
||||
|
||||
# loading image and label
|
||||
if self.is_train:
|
||||
image, _ = preproc(image, image, preproc_methods=config.preproc_methods)
|
||||
|
||||
image = self.transform_image(image)
|
||||
size = self.img_sizes[index]
|
||||
return image, size
|
||||
|
||||
def __len__(self):
|
||||
return len(self.image_paths)
|
||||
@@ -0,0 +1,162 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from BiRefNet.modules.deform_conv import DeformableConv2d
|
||||
from ..config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
class ASPPComplex(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=None, output_stride=16):
|
||||
super(ASPPComplex, self).__init__()
|
||||
self.down_scale = 1
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
self.in_channelster = 256 // self.down_scale
|
||||
if output_stride == 16:
|
||||
dilations = [1, 6, 12, 18]
|
||||
elif output_stride == 8:
|
||||
dilations = [1, 12, 24, 36]
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0])
|
||||
self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1])
|
||||
self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2])
|
||||
self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3])
|
||||
|
||||
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
|
||||
nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
|
||||
nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
|
||||
nn.ReLU(inplace=True))
|
||||
self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.dropout = nn.Dropout(0.5)
|
||||
|
||||
def forward(self, x):
|
||||
x1 = self.aspp1(x)
|
||||
x2 = self.aspp2(x)
|
||||
x3 = self.aspp3(x)
|
||||
x4 = self.aspp4(x)
|
||||
x5 = self.global_avg_pool(x)
|
||||
x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
|
||||
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
|
||||
return self.dropout(x)
|
||||
|
||||
|
||||
class _ASPPModule(nn.Module):
|
||||
def __init__(self, in_channels, planes, kernel_size, padding, dilation):
|
||||
super(_ASPPModule, self).__init__()
|
||||
self.atrous_conv = nn.Conv2d(in_channels, planes, kernel_size=kernel_size,
|
||||
stride=1, padding=padding, dilation=dilation, bias=False)
|
||||
self.bn = nn.BatchNorm2d(planes)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.atrous_conv(x)
|
||||
x = self.bn(x)
|
||||
|
||||
return self.relu(x)
|
||||
|
||||
class ASPP(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=None, output_stride=16):
|
||||
super(ASPP, self).__init__()
|
||||
self.down_scale = 1
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
self.in_channelster = 256 // self.down_scale
|
||||
if output_stride == 16:
|
||||
dilations = [1, 6, 12, 18]
|
||||
elif output_stride == 8:
|
||||
dilations = [1, 12, 24, 36]
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
self.aspp1 = _ASPPModule(in_channels, self.in_channelster, 1, padding=0, dilation=dilations[0])
|
||||
self.aspp2 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[1], dilation=dilations[1])
|
||||
self.aspp3 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[2], dilation=dilations[2])
|
||||
self.aspp4 = _ASPPModule(in_channels, self.in_channelster, 3, padding=dilations[3], dilation=dilations[3])
|
||||
|
||||
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
|
||||
nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
|
||||
nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
|
||||
nn.ReLU(inplace=True))
|
||||
self.conv1 = nn.Conv2d(self.in_channelster * 5, out_channels, 1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.dropout = nn.Dropout(0.5)
|
||||
|
||||
def forward(self, x):
|
||||
x1 = self.aspp1(x)
|
||||
x2 = self.aspp2(x)
|
||||
x3 = self.aspp3(x)
|
||||
x4 = self.aspp4(x)
|
||||
x5 = self.global_avg_pool(x)
|
||||
x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
|
||||
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
|
||||
return self.dropout(x)
|
||||
|
||||
|
||||
##################### Deformable
|
||||
class _ASPPModuleDeformable(nn.Module):
|
||||
def __init__(self, in_channels, planes, kernel_size, padding):
|
||||
super(_ASPPModuleDeformable, self).__init__()
|
||||
self.atrous_conv = DeformableConv2d(in_channels, planes, kernel_size=kernel_size,
|
||||
stride=1, padding=padding, bias=False)
|
||||
self.bn = nn.BatchNorm2d(planes)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.atrous_conv(x)
|
||||
x = self.bn(x)
|
||||
|
||||
return self.relu(x)
|
||||
|
||||
|
||||
class ASPPDeformable(nn.Module):
|
||||
def __init__(self, in_channels, out_channels=None, num_parallel_block=1):
|
||||
super(ASPPDeformable, self).__init__()
|
||||
self.down_scale = 1
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
self.in_channelster = 256 // self.down_scale
|
||||
|
||||
self.aspp1 = _ASPPModuleDeformable(in_channels, self.in_channelster, 1, padding=0)
|
||||
self.aspp_deforms = nn.ModuleList([
|
||||
_ASPPModuleDeformable(in_channels, self.in_channelster, 3, padding=1) for _ in range(num_parallel_block)
|
||||
])
|
||||
|
||||
self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
|
||||
nn.Conv2d(in_channels, self.in_channelster, 1, stride=1, bias=False),
|
||||
nn.BatchNorm2d(self.in_channelster) if config.batch_size > 1 else nn.Identity(),
|
||||
nn.ReLU(inplace=True))
|
||||
self.conv1 = nn.Conv2d(self.in_channelster * (2 + len(self.aspp_deforms)), out_channels, 1, bias=False)
|
||||
self.bn1 = nn.BatchNorm2d(out_channels)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.dropout = nn.Dropout(0.5)
|
||||
|
||||
def forward(self, x):
|
||||
x1 = self.aspp1(x)
|
||||
x_aspp_deforms = [aspp_deform(x) for aspp_deform in self.aspp_deforms]
|
||||
x5 = self.global_avg_pool(x)
|
||||
x5 = F.interpolate(x5, size=x1.size()[2:], mode='bilinear', align_corners=True)
|
||||
x = torch.cat((x1, *x_aspp_deforms, x5), dim=1)
|
||||
|
||||
x = self.conv1(x)
|
||||
x = self.bn1(x)
|
||||
x = self.relu(x)
|
||||
|
||||
return self.dropout(x)
|
||||
@@ -0,0 +1,93 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import init
|
||||
|
||||
|
||||
class SEWeightModule(nn.Module):
|
||||
def __init__(self, channels, reduction=16):
|
||||
super(SEWeightModule, self).__init__()
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
||||
self.fc1 = nn.Conv2d(channels, channels//reduction, kernel_size=1, padding=0)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.fc2 = nn.Conv2d(channels//reduction, channels, kernel_size=1, padding=0)
|
||||
self.sigmoid = nn.Sigmoid()
|
||||
|
||||
def forward(self, x):
|
||||
out = self.avg_pool(x)
|
||||
out = self.fc1(out)
|
||||
out = self.relu(out)
|
||||
out = self.fc2(out)
|
||||
weight = self.sigmoid(out)
|
||||
return weight
|
||||
|
||||
|
||||
class PSA(nn.Module):
|
||||
|
||||
def __init__(self, in_channels, S=4, reduction=4):
|
||||
super().__init__()
|
||||
self.S = S
|
||||
|
||||
_convs = []
|
||||
for i in range(S):
|
||||
_convs.append(nn.Conv2d(in_channels//S, in_channels//S, kernel_size=2*(i+1)+1, padding=i+1))
|
||||
self.convs = nn.ModuleList(_convs)
|
||||
|
||||
self.se_block = SEWeightModule(in_channels//S, reduction=S*reduction)
|
||||
|
||||
self.softmax = nn.Softmax(dim=1)
|
||||
|
||||
def forward(self, x):
|
||||
b, c, h, w = x.size()
|
||||
|
||||
# Step1: SPC module
|
||||
SPC_out = x.view(b, self.S, c//self.S, h, w) #bs,s,ci,h,w
|
||||
for idx, conv in enumerate(self.convs):
|
||||
SPC_out[:,idx,:,:,:] = conv(SPC_out[:,idx,:,:,:].clone())
|
||||
|
||||
# Step2: SE weight
|
||||
se_out=[]
|
||||
for idx in range(self.S):
|
||||
se_out.append(self.se_block(SPC_out[:, idx, :, :, :]))
|
||||
SE_out = torch.stack(se_out, dim=1)
|
||||
SE_out = SE_out.expand_as(SPC_out)
|
||||
|
||||
# Step3: Softmax
|
||||
softmax_out = self.softmax(SE_out)
|
||||
|
||||
# Step4: SPA
|
||||
PSA_out = SPC_out * softmax_out
|
||||
PSA_out = PSA_out.view(b, -1, h, w)
|
||||
|
||||
return PSA_out
|
||||
|
||||
|
||||
class SGE(nn.Module):
|
||||
|
||||
def __init__(self, groups):
|
||||
super().__init__()
|
||||
self.groups=groups
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
||||
self.weight=nn.Parameter(torch.zeros(1,groups,1,1))
|
||||
self.bias=nn.Parameter(torch.zeros(1,groups,1,1))
|
||||
self.sig=nn.Sigmoid()
|
||||
|
||||
def forward(self, x):
|
||||
b, c, h,w=x.shape
|
||||
x=x.view(b*self.groups,-1,h,w) #bs*g,dim//g,h,w
|
||||
xn=x*self.avg_pool(x) #bs*g,dim//g,h,w
|
||||
xn=xn.sum(dim=1,keepdim=True) #bs*g,1,h,w
|
||||
t=xn.view(b*self.groups,-1) #bs*g,h*w
|
||||
|
||||
t=t-t.mean(dim=1,keepdim=True) #bs*g,h*w
|
||||
std=t.std(dim=1,keepdim=True)+1e-5
|
||||
t=t/std #bs*g,h*w
|
||||
t=t.view(b,self.groups,h,w) #bs,g,h*w
|
||||
|
||||
t=t*self.weight+self.bias #bs,g,h*w
|
||||
t=t.view(b*self.groups,1,h,w) #bs*g,1,h*w
|
||||
x=x*self.sig(t)
|
||||
x=x.view(b,c,h,w)
|
||||
|
||||
return x
|
||||
|
||||
@@ -0,0 +1,101 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from BiRefNet.modules.aspp import ASPP, ASPPDeformable
|
||||
from BiRefNet.modules.attentions import PSA, SGE
|
||||
from ..config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
class BasicDecBlk(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
|
||||
super(BasicDecBlk, self).__init__()
|
||||
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
|
||||
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
|
||||
self.relu_in = nn.ReLU(inplace=True)
|
||||
if config.dec_att == 'ASPP':
|
||||
self.dec_att = ASPP(in_channels=inter_channels)
|
||||
elif config.dec_att == 'ASPPDeformable':
|
||||
self.dec_att = ASPPDeformable(in_channels=inter_channels)
|
||||
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
|
||||
self.bn_in = nn.BatchNorm2d(inter_channels)
|
||||
self.bn_out = nn.BatchNorm2d(out_channels)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv_in(x)
|
||||
x = self.bn_in(x)
|
||||
x = self.relu_in(x)
|
||||
if hasattr(self, 'dec_att'):
|
||||
x = self.dec_att(x)
|
||||
x = self.conv_out(x)
|
||||
x = self.bn_out(x)
|
||||
return x
|
||||
|
||||
|
||||
class ResBlk(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
|
||||
super(ResBlk, self).__init__()
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
|
||||
|
||||
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, padding=1)
|
||||
self.bn_in = nn.BatchNorm2d(inter_channels)
|
||||
self.relu_in = nn.ReLU(inplace=True)
|
||||
|
||||
if config.dec_att == 'ASPP':
|
||||
self.dec_att = ASPP(in_channels=inter_channels)
|
||||
elif config.dec_att == 'ASPPDeformable':
|
||||
self.dec_att = ASPPDeformable(in_channels=inter_channels)
|
||||
|
||||
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, padding=1)
|
||||
self.bn_out = nn.BatchNorm2d(out_channels)
|
||||
|
||||
self.conv_resi = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
|
||||
|
||||
def forward(self, x):
|
||||
_x = self.conv_resi(x)
|
||||
x = self.conv_in(x)
|
||||
x = self.bn_in(x)
|
||||
x = self.relu_in(x)
|
||||
if hasattr(self, 'dec_att'):
|
||||
x = self.dec_att(x)
|
||||
x = self.conv_out(x)
|
||||
x = self.bn_out(x)
|
||||
return x + _x
|
||||
|
||||
|
||||
class HierarAttDecBlk(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=None, inter_channels=64):
|
||||
super(HierarAttDecBlk, self).__init__()
|
||||
if out_channels is None:
|
||||
out_channels = in_channels
|
||||
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
|
||||
self.split_y = 8 # must be divided by channels of all intermediate features
|
||||
self.split_x = 8
|
||||
|
||||
self.conv_in = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
|
||||
|
||||
self.psa = PSA(inter_channels*self.split_y*self.split_x, S=config.batch_size)
|
||||
self.sge = SGE(groups=config.batch_size)
|
||||
|
||||
if config.dec_att == 'ASPP':
|
||||
self.dec_att = ASPP(in_channels=inter_channels)
|
||||
elif config.dec_att == 'ASPPDeformable':
|
||||
self.dec_att = ASPPDeformable(in_channels=inter_channels)
|
||||
self.conv_out = nn.Conv2d(inter_channels, out_channels, 3, 1, 1)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv_in(x)
|
||||
N, C, H, W = x.shape
|
||||
x_patchs = x.reshape(N, -1, H//self.split_y, W//self.split_x)
|
||||
|
||||
# Hierarchical attention: group attention X patch spatial attention
|
||||
x_patchs = self.psa(x_patchs) # Group Channel Attention -- each group is a single image
|
||||
x_patchs = self.sge(x_patchs) # Patch Spatial Attention
|
||||
x = x.reshape(N, C, H, W)
|
||||
if hasattr(self, 'dec_att'):
|
||||
x = self.dec_att(x)
|
||||
x = self.conv_out(x)
|
||||
return x
|
||||
@@ -0,0 +1,66 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torchvision.ops import deform_conv2d
|
||||
|
||||
|
||||
class DeformableConv2d(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1,
|
||||
bias=False):
|
||||
|
||||
super(DeformableConv2d, self).__init__()
|
||||
|
||||
assert type(kernel_size) == tuple or type(kernel_size) == int
|
||||
|
||||
kernel_size = kernel_size if type(kernel_size) == tuple else (kernel_size, kernel_size)
|
||||
self.stride = stride if type(stride) == tuple else (stride, stride)
|
||||
self.padding = padding
|
||||
|
||||
self.offset_conv = nn.Conv2d(in_channels,
|
||||
2 * kernel_size[0] * kernel_size[1],
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=self.padding,
|
||||
bias=True)
|
||||
|
||||
nn.init.constant_(self.offset_conv.weight, 0.)
|
||||
nn.init.constant_(self.offset_conv.bias, 0.)
|
||||
|
||||
self.modulator_conv = nn.Conv2d(in_channels,
|
||||
1 * kernel_size[0] * kernel_size[1],
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=self.padding,
|
||||
bias=True)
|
||||
|
||||
nn.init.constant_(self.modulator_conv.weight, 0.)
|
||||
nn.init.constant_(self.modulator_conv.bias, 0.)
|
||||
|
||||
self.regular_conv = nn.Conv2d(in_channels,
|
||||
out_channels=out_channels,
|
||||
kernel_size=kernel_size,
|
||||
stride=stride,
|
||||
padding=self.padding,
|
||||
bias=bias)
|
||||
|
||||
def forward(self, x):
|
||||
#h, w = x.shape[2:]
|
||||
#max_offset = max(h, w)/4.
|
||||
|
||||
offset = self.offset_conv(x)#.clamp(-max_offset, max_offset)
|
||||
modulator = 2. * torch.sigmoid(self.modulator_conv(x))
|
||||
|
||||
x = deform_conv2d(
|
||||
input=x,
|
||||
offset=offset,
|
||||
weight=self.regular_conv.weight,
|
||||
bias=self.regular_conv.bias,
|
||||
padding=self.padding,
|
||||
mask=modulator,
|
||||
stride=self.stride,
|
||||
)
|
||||
return x
|
||||
@@ -0,0 +1,29 @@
|
||||
import torch.nn as nn
|
||||
from BiRefNet.modules.mlp import MLPLayer
|
||||
|
||||
|
||||
class BlockA(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=64, inter_channels=64, mlp_ratio=4.):
|
||||
super(BlockA, self).__init__()
|
||||
inter_channels = in_channels
|
||||
self.conv = nn.Conv2d(in_channels, inter_channels, 3, 1, 1)
|
||||
self.norm1 = nn.LayerNorm(inter_channels)
|
||||
self.ffn = MLPLayer(in_features=inter_channels,
|
||||
hidden_features=int(inter_channels * mlp_ratio),
|
||||
act_layer=nn.GELU,
|
||||
drop=0.)
|
||||
self.norm2 = nn.LayerNorm(inter_channels)
|
||||
|
||||
def forward(self, x):
|
||||
B, C, H, W = x.shape
|
||||
_x = self.conv(x)
|
||||
_x = _x.flatten(2).transpose(1, 2)
|
||||
_x = self.norm1(_x)
|
||||
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
|
||||
x = x + _x
|
||||
_x1 = self.ffn(x)
|
||||
_x1 = self.norm2(_x1)
|
||||
_x1 = _x1.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
|
||||
x = x + _x1
|
||||
return x
|
||||
@@ -0,0 +1,21 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from functools import partial
|
||||
|
||||
from ..config import Config
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
|
||||
class BasicLatBlk(nn.Module):
|
||||
def __init__(self, in_channels=64, out_channels=64, inter_channels=64):
|
||||
super(BasicLatBlk, self).__init__()
|
||||
inter_channels = in_channels // 4 if config.dec_channels_inter == 'adap' else 64
|
||||
self.conv = nn.Conv2d(in_channels, out_channels, 1, 1, 0)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv(x)
|
||||
return x
|
||||
@@ -0,0 +1,118 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from functools import partial
|
||||
|
||||
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
|
||||
from timm.models.registry import register_model
|
||||
|
||||
import math
|
||||
|
||||
|
||||
class MLPLayer(nn.Module):
|
||||
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
|
||||
super().__init__()
|
||||
out_features = out_features or in_features
|
||||
hidden_features = hidden_features or in_features
|
||||
self.fc1 = nn.Linear(in_features, hidden_features)
|
||||
self.act = act_layer()
|
||||
self.fc2 = nn.Linear(hidden_features, out_features)
|
||||
self.drop = nn.Dropout(drop)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.fc1(x)
|
||||
x = self.act(x)
|
||||
x = self.drop(x)
|
||||
x = self.fc2(x)
|
||||
x = self.drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
|
||||
super().__init__()
|
||||
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
|
||||
|
||||
self.dim = dim
|
||||
self.num_heads = num_heads
|
||||
head_dim = dim // num_heads
|
||||
self.scale = qk_scale or head_dim ** -0.5
|
||||
|
||||
self.q = nn.Linear(dim, dim, bias=qkv_bias)
|
||||
self.kv = nn.Linear(dim, dim * 2, bias=qkv_bias)
|
||||
self.attn_drop = nn.Dropout(attn_drop)
|
||||
self.proj = nn.Linear(dim, dim)
|
||||
self.proj_drop = nn.Dropout(proj_drop)
|
||||
|
||||
self.sr_ratio = sr_ratio
|
||||
if sr_ratio > 1:
|
||||
self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio)
|
||||
self.norm = nn.LayerNorm(dim)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
B, N, C = x.shape
|
||||
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
|
||||
|
||||
if self.sr_ratio > 1:
|
||||
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
|
||||
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
|
||||
x_ = self.norm(x_)
|
||||
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
else:
|
||||
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
|
||||
k, v = kv[0], kv[1]
|
||||
|
||||
attn = (q @ k.transpose(-2, -1)) * self.scale
|
||||
attn = attn.softmax(dim=-1)
|
||||
attn = self.attn_drop(attn)
|
||||
|
||||
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
||||
x = self.proj(x)
|
||||
x = self.proj_drop(x)
|
||||
return x
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
|
||||
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, sr_ratio=1):
|
||||
super().__init__()
|
||||
self.norm1 = norm_layer(dim)
|
||||
self.attn = Attention(
|
||||
dim,
|
||||
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
|
||||
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
|
||||
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
|
||||
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
|
||||
self.norm2 = norm_layer(dim)
|
||||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||||
self.mlp = MLPLayer(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
|
||||
|
||||
def forward(self, x, H, W):
|
||||
x = x + self.drop_path(self.attn(self.norm1(x), H, W))
|
||||
x = x + self.drop_path(self.mlp(self.norm2(x), H, W))
|
||||
return x
|
||||
|
||||
|
||||
class OverlapPatchEmbed(nn.Module):
|
||||
""" Image to Patch Embedding
|
||||
"""
|
||||
|
||||
def __init__(self, img_size=224, patch_size=7, stride=4, in_channels=3, embed_dim=768):
|
||||
super().__init__()
|
||||
img_size = to_2tuple(img_size)
|
||||
patch_size = to_2tuple(patch_size)
|
||||
|
||||
self.img_size = img_size
|
||||
self.patch_size = patch_size
|
||||
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
|
||||
self.num_patches = self.H * self.W
|
||||
self.proj = nn.Conv2d(in_channels, embed_dim, kernel_size=patch_size, stride=stride,
|
||||
padding=(patch_size[0] // 2, patch_size[1] // 2))
|
||||
self.norm = nn.LayerNorm(embed_dim)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
_, _, H, W = x.shape
|
||||
x = x.flatten(2).transpose(1, 2)
|
||||
x = self.norm(x)
|
||||
return x, H, W
|
||||
|
||||
@@ -0,0 +1,54 @@
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
def build_act_layer(act_layer):
|
||||
if act_layer == 'ReLU':
|
||||
return nn.ReLU(inplace=True)
|
||||
elif act_layer == 'SiLU':
|
||||
return nn.SiLU(inplace=True)
|
||||
elif act_layer == 'GELU':
|
||||
return nn.GELU()
|
||||
|
||||
raise NotImplementedError(f'build_act_layer does not support {act_layer}')
|
||||
|
||||
|
||||
def build_norm_layer(dim,
|
||||
norm_layer,
|
||||
in_format='channels_last',
|
||||
out_format='channels_last',
|
||||
eps=1e-6):
|
||||
layers = []
|
||||
if norm_layer == 'BN':
|
||||
if in_format == 'channels_last':
|
||||
layers.append(to_channels_first())
|
||||
layers.append(nn.BatchNorm2d(dim))
|
||||
if out_format == 'channels_last':
|
||||
layers.append(to_channels_last())
|
||||
elif norm_layer == 'LN':
|
||||
if in_format == 'channels_first':
|
||||
layers.append(to_channels_last())
|
||||
layers.append(nn.LayerNorm(dim, eps=eps))
|
||||
if out_format == 'channels_first':
|
||||
layers.append(to_channels_first())
|
||||
else:
|
||||
raise NotImplementedError(
|
||||
f'build_norm_layer does not support {norm_layer}')
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
|
||||
class to_channels_first(nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return x.permute(0, 3, 1, 2)
|
||||
|
||||
|
||||
class to_channels_last(nn.Module):
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x):
|
||||
return x.permute(0, 2, 3, 1)
|
||||
@@ -0,0 +1,85 @@
|
||||
from PIL import Image, ImageEnhance
|
||||
import random
|
||||
import numpy as np
|
||||
import random
|
||||
|
||||
|
||||
def preproc(image, label, preproc_methods=['flip']):
|
||||
if 'flip' in preproc_methods:
|
||||
image, label = cv_random_flip(image, label)
|
||||
if 'crop' in preproc_methods:
|
||||
image, label = random_crop(image, label)
|
||||
if 'rotate' in preproc_methods:
|
||||
image, label = random_rotate(image, label)
|
||||
if 'enhance' in preproc_methods:
|
||||
image = color_enhance(image)
|
||||
if 'pepper' in preproc_methods:
|
||||
label = random_pepper(label)
|
||||
return image, label
|
||||
|
||||
|
||||
def cv_random_flip(img, label):
|
||||
if random.random() > 0.5:
|
||||
img = img.transpose(Image.FLIP_LEFT_RIGHT)
|
||||
label = label.transpose(Image.FLIP_LEFT_RIGHT)
|
||||
return img, label
|
||||
|
||||
|
||||
def random_crop(image, label):
|
||||
border = 30
|
||||
image_width = image.size[0]
|
||||
image_height = image.size[1]
|
||||
border = int(min(image_width, image_height) * 0.1)
|
||||
crop_win_width = np.random.randint(image_width - border, image_width)
|
||||
crop_win_height = np.random.randint(image_height - border, image_height)
|
||||
random_region = (
|
||||
(image_width - crop_win_width) >> 1, (image_height - crop_win_height) >> 1, (image_width + crop_win_width) >> 1,
|
||||
(image_height + crop_win_height) >> 1)
|
||||
return image.crop(random_region), label.crop(random_region)
|
||||
|
||||
|
||||
def random_rotate(image, label, angle=15):
|
||||
mode = Image.BICUBIC
|
||||
if random.random() > 0.8:
|
||||
random_angle = np.random.randint(-angle, angle)
|
||||
image = image.rotate(random_angle, mode)
|
||||
label = label.rotate(random_angle, mode)
|
||||
return image, label
|
||||
|
||||
|
||||
def color_enhance(image):
|
||||
bright_intensity = random.randint(5, 15) / 10.0
|
||||
image = ImageEnhance.Brightness(image).enhance(bright_intensity)
|
||||
contrast_intensity = random.randint(5, 15) / 10.0
|
||||
image = ImageEnhance.Contrast(image).enhance(contrast_intensity)
|
||||
color_intensity = random.randint(0, 20) / 10.0
|
||||
image = ImageEnhance.Color(image).enhance(color_intensity)
|
||||
sharp_intensity = random.randint(0, 30) / 10.0
|
||||
image = ImageEnhance.Sharpness(image).enhance(sharp_intensity)
|
||||
return image
|
||||
|
||||
|
||||
def random_gaussian(image, mean=0.1, sigma=0.35):
|
||||
def gaussianNoisy(im, mean=mean, sigma=sigma):
|
||||
for _i in range(len(im)):
|
||||
im[_i] += random.gauss(mean, sigma)
|
||||
return im
|
||||
|
||||
img = np.asarray(image)
|
||||
width, height = img.shape
|
||||
img = gaussianNoisy(img[:].flatten(), mean, sigma)
|
||||
img = img.reshape([width, height])
|
||||
return Image.fromarray(np.uint8(img))
|
||||
|
||||
|
||||
def random_pepper(img, N=0.0015):
|
||||
img = np.array(img)
|
||||
noiseNum = int(N * img.shape[0] * img.shape[1])
|
||||
for i in range(noiseNum):
|
||||
randX = random.randint(0, img.shape[0] - 1)
|
||||
randY = random.randint(0, img.shape[1] - 1)
|
||||
if random.randint(0, 1) == 0:
|
||||
img[randX, randY] = 0
|
||||
else:
|
||||
img[randX, randY] = 255
|
||||
return Image.fromarray(img)
|
||||
@@ -0,0 +1,253 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from collections import OrderedDict
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torchvision.models import vgg16, vgg16_bn
|
||||
from torchvision.models import resnet50
|
||||
|
||||
from ..config import Config
|
||||
from ..dataset import class_labels_TR_sorted
|
||||
from BiRefNet.backbones.build_backbone import build_backbone
|
||||
from BiRefNet.modules.decoder_blocks import BasicDecBlk
|
||||
from BiRefNet.modules.lateral_blocks import BasicLatBlk
|
||||
from BiRefNet.modules.ing import *
|
||||
from BiRefNet.refinement.stem_layer import StemLayer
|
||||
|
||||
|
||||
class RefinerPVTInChannels4(nn.Module):
|
||||
def __init__(self, in_channels=3+1):
|
||||
super(RefinerPVTInChannels4, self).__init__()
|
||||
self.config = Config()
|
||||
self.epoch = 1
|
||||
self.bb = build_backbone(self.config.bb, params_settings='in_channels=4')
|
||||
|
||||
lateral_channels_in_collection = {
|
||||
'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
|
||||
'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
|
||||
'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
|
||||
}
|
||||
channels = lateral_channels_in_collection[self.config.bb]
|
||||
self.squeeze_module = BasicDecBlk(channels[0], channels[0])
|
||||
|
||||
self.decoder = Decoder(channels)
|
||||
|
||||
if 0:
|
||||
for key, value in self.named_parameters():
|
||||
if 'bb.' in key:
|
||||
value.requires_grad = False
|
||||
|
||||
def forward(self, x):
|
||||
if isinstance(x, list):
|
||||
x = torch.cat(x, dim=1)
|
||||
########## Encoder ##########
|
||||
if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
|
||||
x1 = self.bb.conv1(x)
|
||||
x2 = self.bb.conv2(x1)
|
||||
x3 = self.bb.conv3(x2)
|
||||
x4 = self.bb.conv4(x3)
|
||||
else:
|
||||
x1, x2, x3, x4 = self.bb(x)
|
||||
|
||||
x4 = self.squeeze_module(x4)
|
||||
|
||||
########## Decoder ##########
|
||||
|
||||
features = [x, x1, x2, x3, x4]
|
||||
scaled_preds = self.decoder(features)
|
||||
|
||||
return scaled_preds
|
||||
|
||||
|
||||
class Refiner(nn.Module):
|
||||
def __init__(self, in_channels=3+1):
|
||||
super(Refiner, self).__init__()
|
||||
self.config = Config()
|
||||
self.epoch = 1
|
||||
self.stem_layer = StemLayer(in_channels=in_channels, inter_channels=48, out_channels=3)
|
||||
self.bb = build_backbone(self.config.bb)
|
||||
|
||||
lateral_channels_in_collection = {
|
||||
'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64],
|
||||
'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64],
|
||||
'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192],
|
||||
}
|
||||
channels = lateral_channels_in_collection[self.config.bb]
|
||||
self.squeeze_module = BasicDecBlk(channels[0], channels[0])
|
||||
|
||||
self.decoder = Decoder(channels)
|
||||
|
||||
if 0:
|
||||
for key, value in self.named_parameters():
|
||||
if 'bb.' in key:
|
||||
value.requires_grad = False
|
||||
|
||||
def forward(self, x):
|
||||
if isinstance(x, list):
|
||||
x = torch.cat(x, dim=1)
|
||||
x = self.stem_layer(x)
|
||||
########## Encoder ##########
|
||||
if self.config.bb in ['vgg16', 'vgg16bn', 'resnet50']:
|
||||
x1 = self.bb.conv1(x)
|
||||
x2 = self.bb.conv2(x1)
|
||||
x3 = self.bb.conv3(x2)
|
||||
x4 = self.bb.conv4(x3)
|
||||
else:
|
||||
x1, x2, x3, x4 = self.bb(x)
|
||||
|
||||
x4 = self.squeeze_module(x4)
|
||||
|
||||
########## Decoder ##########
|
||||
|
||||
features = [x, x1, x2, x3, x4]
|
||||
scaled_preds = self.decoder(features)
|
||||
|
||||
return scaled_preds
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super(Decoder, self).__init__()
|
||||
self.config = Config()
|
||||
DecoderBlock = eval('BasicDecBlk')
|
||||
LateralBlock = eval('BasicLatBlk')
|
||||
|
||||
self.decoder_block4 = DecoderBlock(channels[0], channels[1])
|
||||
self.decoder_block3 = DecoderBlock(channels[1], channels[2])
|
||||
self.decoder_block2 = DecoderBlock(channels[2], channels[3])
|
||||
self.decoder_block1 = DecoderBlock(channels[3], channels[3]//2)
|
||||
|
||||
self.lateral_block4 = LateralBlock(channels[1], channels[1])
|
||||
self.lateral_block3 = LateralBlock(channels[2], channels[2])
|
||||
self.lateral_block2 = LateralBlock(channels[3], channels[3])
|
||||
|
||||
if self.config.ms_supervision:
|
||||
self.conv_ms_spvn_4 = nn.Conv2d(channels[1], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_3 = nn.Conv2d(channels[2], 1, 1, 1, 0)
|
||||
self.conv_ms_spvn_2 = nn.Conv2d(channels[3], 1, 1, 1, 0)
|
||||
self.conv_out1 = nn.Sequential(nn.Conv2d(channels[3]//2, 1, 1, 1, 0))
|
||||
|
||||
def forward(self, features):
|
||||
x, x1, x2, x3, x4 = features
|
||||
outs = []
|
||||
p4 = self.decoder_block4(x4)
|
||||
_p4 = F.interpolate(p4, size=x3.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p3 = _p4 + self.lateral_block4(x3)
|
||||
|
||||
p3 = self.decoder_block3(_p3)
|
||||
_p3 = F.interpolate(p3, size=x2.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p2 = _p3 + self.lateral_block3(x2)
|
||||
|
||||
p2 = self.decoder_block2(_p2)
|
||||
_p2 = F.interpolate(p2, size=x1.shape[2:], mode='bilinear', align_corners=True)
|
||||
_p1 = _p2 + self.lateral_block2(x1)
|
||||
|
||||
_p1 = self.decoder_block1(_p1)
|
||||
_p1 = F.interpolate(_p1, size=x.shape[2:], mode='bilinear', align_corners=True)
|
||||
p1_out = self.conv_out1(_p1)
|
||||
|
||||
if self.config.ms_supervision:
|
||||
outs.append(self.conv_ms_spvn_4(p4))
|
||||
outs.append(self.conv_ms_spvn_3(p3))
|
||||
outs.append(self.conv_ms_spvn_2(p2))
|
||||
outs.append(p1_out)
|
||||
return outs
|
||||
|
||||
|
||||
class RefUNet(nn.Module):
|
||||
# Refinement
|
||||
def __init__(self, in_channels=3+1):
|
||||
super(RefUNet, self).__init__()
|
||||
self.encoder_1 = nn.Sequential(
|
||||
nn.Conv2d(in_channels, 64, 3, 1, 1),
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.encoder_2 = nn.Sequential(
|
||||
nn.MaxPool2d(2, 2, ceil_mode=True),
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.encoder_3 = nn.Sequential(
|
||||
nn.MaxPool2d(2, 2, ceil_mode=True),
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.encoder_4 = nn.Sequential(
|
||||
nn.MaxPool2d(2, 2, ceil_mode=True),
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.pool4 = nn.MaxPool2d(2, 2, ceil_mode=True)
|
||||
#####
|
||||
self.decoder_5 = nn.Sequential(
|
||||
nn.Conv2d(64, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
#####
|
||||
self.decoder_4 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.decoder_3 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.decoder_2 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.decoder_1 = nn.Sequential(
|
||||
nn.Conv2d(128, 64, 3, 1, 1),
|
||||
nn.BatchNorm2d(64),
|
||||
nn.ReLU(inplace=True)
|
||||
)
|
||||
|
||||
self.conv_d0 = nn.Conv2d(64, 1, 3, 1, 1)
|
||||
|
||||
self.upscore2 = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
|
||||
|
||||
def forward(self, x):
|
||||
outs = []
|
||||
if isinstance(x, list):
|
||||
x = torch.cat(x, dim=1)
|
||||
hx = x
|
||||
|
||||
hx1 = self.encoder_1(hx)
|
||||
hx2 = self.encoder_2(hx1)
|
||||
hx3 = self.encoder_3(hx2)
|
||||
hx4 = self.encoder_4(hx3)
|
||||
|
||||
hx = self.decoder_5(self.pool4(hx4))
|
||||
hx = torch.cat((self.upscore2(hx), hx4), 1)
|
||||
|
||||
d4 = self.decoder_4(hx)
|
||||
hx = torch.cat((self.upscore2(d4), hx3), 1)
|
||||
|
||||
d3 = self.decoder_3(hx)
|
||||
hx = torch.cat((self.upscore2(d3), hx2), 1)
|
||||
|
||||
d2 = self.decoder_2(hx)
|
||||
hx = torch.cat((self.upscore2(d2), hx1), 1)
|
||||
|
||||
d1 = self.decoder_1(hx)
|
||||
|
||||
x = self.conv_d0(d1)
|
||||
outs.append(x)
|
||||
return outs
|
||||
@@ -0,0 +1,45 @@
|
||||
import torch.nn as nn
|
||||
from BiRefNet.modules.utils import build_act_layer, build_norm_layer
|
||||
|
||||
|
||||
class StemLayer(nn.Module):
|
||||
r""" Stem layer of InternImage
|
||||
Args:
|
||||
in_channels (int): number of input channels
|
||||
out_channels (int): number of output channels
|
||||
act_layer (str): activation layer
|
||||
norm_layer (str): normalization layer
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
in_channels=3+1,
|
||||
inter_channels=48,
|
||||
out_channels=96,
|
||||
act_layer='GELU',
|
||||
norm_layer='BN'):
|
||||
super().__init__()
|
||||
self.conv1 = nn.Conv2d(in_channels,
|
||||
inter_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
self.norm1 = build_norm_layer(
|
||||
inter_channels, norm_layer, 'channels_first', 'channels_first'
|
||||
)
|
||||
self.act = build_act_layer(act_layer)
|
||||
self.conv2 = nn.Conv2d(inter_channels,
|
||||
out_channels,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=1)
|
||||
self.norm2 = build_norm_layer(
|
||||
out_channels, norm_layer, 'channels_first', 'channels_first'
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.norm1(x)
|
||||
x = self.act(x)
|
||||
x = self.conv2(x)
|
||||
x = self.norm2(x)
|
||||
return x
|
||||
@@ -0,0 +1,148 @@
|
||||
import torch
|
||||
|
||||
from .imagefunc import *
|
||||
|
||||
import torch.nn as nn
|
||||
from torchvision import transforms
|
||||
from .BiRefNet.baseline import BiRefNet
|
||||
# from .BiRefNet import config
|
||||
from .BiRefNet.config import Config
|
||||
|
||||
NODE_NAME = 'BiRefNetUltra'
|
||||
|
||||
|
||||
config = Config()
|
||||
|
||||
class BiRefNet_img_processor:
|
||||
def __init__(self, config):
|
||||
self.config = config
|
||||
self.data_size = (config.size, config.size)
|
||||
self.transform_image = transforms.Compose([
|
||||
transforms.Resize(self.data_size),
|
||||
transforms.ToTensor(),
|
||||
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
||||
])
|
||||
|
||||
def __call__(self, _image: np.array):
|
||||
_image_rs = cv2.resize(_image, (self.config.size, self.config.size), interpolation=cv2.INTER_LINEAR)
|
||||
_image_rs = Image.fromarray(np.uint8(_image_rs*255)).convert('RGB')
|
||||
image = self.transform_image(_image_rs)
|
||||
return image
|
||||
|
||||
class BiRefNetUltra:
|
||||
def __init__(self):
|
||||
self.ready = False
|
||||
|
||||
def load(self, weight_path, device):
|
||||
# load model
|
||||
self.model = BiRefNet()
|
||||
state_dict = torch.load(weight_path, map_location='cpu')
|
||||
unwanted_prefix = '_orig_mod.'
|
||||
for k, v in list(state_dict.items()):
|
||||
if k.startswith(unwanted_prefix):
|
||||
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
|
||||
self.model.load_state_dict(state_dict)
|
||||
self.model = self.model.to(device)
|
||||
self.model.eval()
|
||||
# load processor
|
||||
self.processor = BiRefNet_img_processor(config)
|
||||
self.ready = True
|
||||
|
||||
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
|
||||
method_list = ['VITMatte', 'PyMatting', 'GuidedFilter']
|
||||
|
||||
|
||||
return {
|
||||
"required": {
|
||||
"image": ("IMAGE",),
|
||||
"detail_method": (method_list,),
|
||||
"detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
|
||||
"detail_dilate": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}),
|
||||
"black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01}),
|
||||
"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01}),
|
||||
"process_detail": ("BOOLEAN", {"default": True}),
|
||||
},
|
||||
"optional": {
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("IMAGE", "MASK", )
|
||||
RETURN_NAMES = ("image", "mask", )
|
||||
FUNCTION = "birefnet_ultra"
|
||||
CATEGORY = '😺dzNodes/LayerMask'
|
||||
|
||||
def birefnet_ultra(self, image, detail_method, detail_erode, detail_dilate,
|
||||
black_point, white_point, process_detail):
|
||||
ret_images = []
|
||||
ret_masks = []
|
||||
|
||||
if torch.backends.mps.is_available():
|
||||
device = "mps"
|
||||
elif torch.cuda.is_available():
|
||||
device = "cuda"
|
||||
else:
|
||||
device = "cpu"
|
||||
|
||||
if not self.ready:
|
||||
model_folder_name = 'BiRefNet'
|
||||
model_name = 'BiRefNet-ep480.pth'
|
||||
model_file_path = ""
|
||||
try:
|
||||
model_file_path = os.path.join(
|
||||
os.path.normpath(folder_paths.folder_names_and_paths[model_folder_name][0][0]), model_name)
|
||||
except:
|
||||
pass
|
||||
if not os.path.exists(model_file_path):
|
||||
model_file_path = os.path.join(folder_paths.models_dir, model_folder_name, model_name)
|
||||
self.load(model_file_path, device=device)
|
||||
|
||||
for i in image:
|
||||
i = torch.unsqueeze(i, 0)
|
||||
orig_image = tensor2pil(i).convert('RGB')
|
||||
np_image = i.squeeze().numpy()
|
||||
img = self.processor(np_image)
|
||||
inputs = img[None, ...].to(device)
|
||||
with torch.no_grad():
|
||||
scaled_preds = self.model(inputs)[-1].sigmoid()
|
||||
_mask = nn.functional.interpolate(
|
||||
scaled_preds[0].unsqueeze(0),
|
||||
size=np_image.shape[:2],
|
||||
mode='bilinear',
|
||||
align_corners=True
|
||||
)[0]
|
||||
|
||||
detail_range = detail_erode + detail_dilate
|
||||
if process_detail:
|
||||
if detail_method == 'GuidedFilter':
|
||||
_mask = guided_filter_alpha(i, _mask, detail_range // 6 + 1)
|
||||
_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
|
||||
elif detail_method == 'PyMatting':
|
||||
_mask = tensor2pil(mask_edge_detail(i, _mask, detail_range // 8 + 1, black_point, white_point))
|
||||
else:
|
||||
brightness_image = ImageEnhance.Brightness(tensor2pil(_mask))
|
||||
_mask = brightness_image.enhance(factor=1.01)
|
||||
_mask = pil2tensor(_mask)
|
||||
_trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate)
|
||||
_mask = generate_VITMatte(orig_image, _trimap)
|
||||
_mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point))
|
||||
else:
|
||||
_mask = tensor2pil(_mask)
|
||||
|
||||
ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
|
||||
ret_images.append(pil2tensor(ret_image))
|
||||
ret_masks.append(image2mask(_mask))
|
||||
|
||||
log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish')
|
||||
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
|
||||
# return (None, torch.cat(ret_masks, dim=0),)
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"LayerMask: BiRefNetUltra": BiRefNetUltra,
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"LayerMask: BiRefNetUltra": "LayerMask: BiRefNetUltra",
|
||||
}
|
||||
@@ -113,6 +113,13 @@ def tensor2np(tensor: torch.Tensor) -> List[np.ndarray]:
|
||||
def tensor2pil(t_image: torch.Tensor) -> Image:
|
||||
return Image.fromarray(np.clip(255.0 * t_image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8))
|
||||
|
||||
def tensor2cv2(image:torch.Tensor) -> np.array:
|
||||
if image.dim() == 4:
|
||||
image = image.squeeze()
|
||||
npimage = image.numpy()
|
||||
cv2image = np.uint8(npimage * 255 / npimage.max())
|
||||
return cv2.cvtColor(cv2image, cv2.COLOR_RGB2BGR)
|
||||
|
||||
def image2mask(image:Image) -> torch.Tensor:
|
||||
_image = image.convert('RGBA')
|
||||
alpha = _image.split() [0]
|
||||
|
||||
+2
-1
@@ -21,4 +21,5 @@ google-generativeai
|
||||
diffusers
|
||||
omegaconf
|
||||
tqdm
|
||||
transformers
|
||||
transformers
|
||||
kornia
|
||||
Reference in New Issue
Block a user