diff --git a/README.MD b/README.MD index bd2d4c2..815dec2 100644 --- a/README.MD +++ b/README.MD @@ -100,10 +100,13 @@ Solution: ### Requests.exceptions.ProxyError: HTTPSConnectionPool(xxxx...) When this error has occurred, please check the network environment. - ## Update **If the dependency package error after updating, please double clicking ```repair_dependency.bat``` (for Official ComfyUI Protable) or ```repair_dependency_aki.bat``` (for ComfyUI-aki-v1.x) in the plugin folder to reinstall the dependency packages.
+* Commit [SAM2Ultra](#SAM2Ultra), [SAM2VideoUltra](#SAM2VideoUltra), [ObjectDetectorFL2](#ObjectDetectorFL2), [ObjectDetectorYOLOWorld](#ObjectDetectorYOLOWorld), [ObjectDetectorYOLO8](#ObjectDetectorYOLO8), [ObjectDetectorMask](#ObjectDetectorMask) and [BBoxJoin](#BBoxJoin) nodes. +Download models from [BaiduNetdisk](https://pan.baidu.com/s/1xaQYBA6ktxvAxm310HXweQ?pwd=auki) or [huggingface.co/Kijai/sam2-safetensors](https://huggingface.co/Kijai/sam2-safetensors/tree/main) and copy to ```ComfyUI/models/sam2``` folder, +Download models from [BaiduNetdisk](https://pan.baidu.com/s/1QpjajeTA37vEAU2OQnbDcQ?pwd=nqsk) or [GoogleDrive](https://drive.google.com/drive/folders/1nrsfq4S-yk9ewJgwrhXAoNVqIFLZ1at7?usp=sharing) and copy to ```ComfyUI/models/yolo-world``` folder. +This update introduces new dependencies, please reinstall the dependency package. * Commit [RandomGenerator](#RandomGenerator) node, Used to generate random numbers within a specified range, with outputs of int, float, and boolean, supporting batch generation of different random numbers by image batch. * Commit [EVF-SAMUltra](#EVFSAMUltra) node, it is implementation of [EVF-SAM](https://github.com/hustvl/EVF-SAM) in ComfyUI. Please download model files from [BaiduNetdisk](https://pan.baidu.com/s/1EvaxgKcCxUpMbYKzLnEx9w?pwd=69bn) or [huggingface/EVF-SAM2](https://huggingface.co/YxZhang/evf-sam2/tree/main), [huggingface/EVF-SAM](https://huggingface.co/YxZhang/evf-sam/tree/main) to ```ComfyUI/models/EVF-SAM``` folder(save the models in their respective subdirectories). Due to the introduction of new dependencies package, after the plugin upgrade, please reinstall the dependency packages. @@ -1588,6 +1591,115 @@ On the basis of SegmentAnythingUltra, the following changes have been made: * device: Set whether the VitMatte to use cuda. * max_megapixels: Set the maximum size for VitMate operations. + +### SAM2Ultra +This node is modified from [kijai/ComfyUI-segment-anything-2](https://github.com/kijai/ComfyUI-segment-anything-2). Thank to [kijai](https://github.com/kijai) for making significant contributions to the Comfyui community. +SAM2 Ultra node only support single image. If you need to process multiple images, please first convert the image batch to image list. +*Download models from [BaiduNetdisk](https://pan.baidu.com/s/1xaQYBA6ktxvAxm310HXweQ?pwd=auki) or [huggingface.co/Kijai/sam2-safetensors](https://huggingface.co/Kijai/sam2-safetensors/tree/main) and copy to ```ComfyUI/models/sam2``` folder. + +![image](image/sam2_example.jpg) + +Node Options: +![image](image/sam2_ultra_node.jpg) + +* image: The image to segment. +* bboxes: Input recognition box data. +* sam2_model: Select the SAM2 model. +* presicion: Model's persicion. can be selected from fp16, bf16, and fp32. +* bbox_select: Select the input box data. There are three options: "all" to select all, "first" to select the box with the highest confidence, and "by_index" to specify the index of the box. +* select_index: This option is valid when bbox_delect is 'by_index'. 0 is the first one. Multiple values can be entered, separated by any non numeric character, including but not limited to commas, periods, semicolons, spaces or letters, and even Chinese. +* cache_model: Whether to cache the model. After caching the model, it will save time for model loading. +* detail_method: Edge processing methods. provides VITMatte, VITMatte(local), PyMatting, GuidedFilter. If the model has been downloaded after the first use of VITMatte, you can use VITMatte (local) afterwards. +* detail_erode: Mask the erosion range inward from the edge. the larger the value, the larger the range of inward repair. +* detail_dilate: The edge of the mask expands outward. the larger the value, the wider the range of outward repair. +* black_point: Edge black sampling threshold. +* white_point: Edge white sampling threshold. +* process_detail: Set to false here will skip edge processing to save runtime. +* device: Set whether the VitMatte to use cuda. +* max_megapixels: Set the maximum size for VitMate operations. + +### SAM2VideoUltra +SAM2 Video Ultra node support processing multiple frames of images or video sequences. Please define the recognition box data in the first frame of the sequence to ensure correct recognition. + + +https://github.com/user-attachments/assets/4726b8bf-9b98-4630-8f54-cb7ed7a3d2c5 + + +Node Options: +![image](image/sam2_video_ultra_node.jpg) + +* image: The image to segment. +* bboxes: Input recognition box data. +* pre_mask: Optional input mask, which will serve as a focus range limitation and help improve recognition accuracy. +* sam2_model: Select the SAM2 model. +* presicion: Model's persicion. can be selected from fp16, bf16, and fp32. +* cache_model: Whether to cache the model. After caching the model, it will save time for model loading. +* individual_object: When set to True, it will focus on identifying a single object. When set to False, attempts will be made to generate recognition boxes for multiple objects. +* mask_preview_color: Display the color of non masked areas in the preview output. +* detail_method: Edge processing methods. Only VITMatte method can be used. +* detail_erode: Mask the erosion range inward from the edge. the larger the value, the larger the range of inward repair. +* detail_dilate: The edge of the mask expands outward. the larger the value, the wider the range of outward repair. +* black_point: Edge black sampling threshold. +* white_point: Edge white sampling threshold. +* process_detail: Set to false here will skip edge processing to save runtime. +* device: Only cuda can be used. +* max_megapixels: Set the maximum size for VitMate operations.A larger size will result in finer mask edges, but it will lead to a significant decrease in computation speed. + +### ObjectDetectorFL2 +Use the Florence2 model to identify objects in images and output recognition box data. +*Download models from [BaiduNetdisk](https://pan.baidu.com/s/1hzw9-QiU1vB8pMbBgofZIA?pwd=mfl3) and copy to ```ComfyUI/models/florence2``` folder. + +Node Options: +![image](image/object_detector_fl2_node.jpg) +* image: The image to segment. +* florence2_model: Florence2 model, it from [LoadFlorence2Model](#LoadFlorence2Model) node. +* prompt: Describe the object that needs to be identified. +* bbox_select: Select the input box data. There are three options: "all" to select all, "first" to select the box with the highest confidence, and "by_index" to specify the index of the box. +* select_index: This option is valid when bbox_delect is 'by_index'. 0 is the first one. Multiple values can be entered, separated by any non numeric character, including but not limited to commas, periods, semicolons, spaces or letters, and even Chinese. + +### ObjectDetectorYOLOWorld +Use the YOLO-World model to identify objects in images and output recognition box data. +*Download models from [BaiduNetdisk](https://pan.baidu.com/s/1QpjajeTA37vEAU2OQnbDcQ?pwd=nqsk) or [GoogleDrive](https://drive.google.com/drive/folders/1nrsfq4S-yk9ewJgwrhXAoNVqIFLZ1at7?usp=sharing) and copy to ```ComfyUI/models/yolo-world``` folder. + +Node Options: +![image](image/object_detector_yolo_world_node.jpg) +* image: The image to segment. +* confidence_threshold: The threshold of confidence. +* nms_iou_threshold: The threshold of Non-Maximum Suppression. +* prompt: Describe the object that needs to be identified. +* bbox_select: Select the input box data. There are three options: "all" to select all, "first" to select the box with the highest confidence, and "by_index" to specify the index of the box. +* select_index: This option is valid when bbox_delect is 'by_index'. 0 is the first one. Multiple values can be entered, separated by any non numeric character, including but not limited to commas, periods, semicolons, spaces or letters, and even Chinese. + +### ObjectDetectorYOLO8 +Use the YOLO-8 model to identify objects in images and output recognition box data. +*Download models from [GoogleDrive](https://drive.google.com/drive/folders/1I5TISO2G1ArSkKJu1O9b4Uvj3DVgn5d2) or [BaiduNetdisk](https://pan.baidu.com/s/1pEY6sjABQaPs6QtpK0q6XA?pwd=grqe) and copy to ```ComfyUI/models/yolo``` folder. + +Node Options: +![image](image/object_detector_yolo8_node.jpg) +* image: The image to segment. +* yolo_model: Choose the yolo model. +* bbox_select: Select the input box data. There are three options: "all" to select all, "first" to select the box with the highest confidence, and "by_index" to specify the index of the box. +* select_index: This option is valid when bbox_delect is 'by_index'. 0 is the first one. Multiple values can be entered, separated by any non numeric character, including but not limited to commas, periods, semicolons, spaces or letters, and even Chinese. + +### ObjectDetectorMask +Use mask as recognition box data. All areas surrounded by white areas on the mask will be recognized as an object. Multiple enclosed areas will be identified separately. + +Node Options: +![image](image/object_detector_mask_node.jpg) +* object_mask: The mask input. +* bbox_select: Select the input box data. There are three options: "all" to select all, "first" to select the box with the highest confidence, and "by_index" to specify the index of the box. +* select_index: This option is valid when bbox_delect is 'by_index'. 0 is the first one. Multiple values can be entered, separated by any non numeric character, including but not limited to commas, periods, semicolons, spaces or letters, and even Chinese. + +### BBoxJoin +Merge recognition box data. + +Node Options: +![image](image/bbox_join_node.jpg) +* bboxes_1: Required input. The first set of identification boxes. +* bboxes_2: Optional input. The second set of identification boxes. +* bboxes_3: Optional input. The third set of identification boxes. +* bboxes_4: Optional input. The fourth set of identification boxes. + ### EVF-SAMUltra This node is implementation of [EVF-SAM](https://github.com/hustvl/EVF-SAM) in ComfyUI. *Please download model files from [BaiduNetdisk](https://pan.baidu.com/s/1EvaxgKcCxUpMbYKzLnEx9w?pwd=69bn) or [huggingface/EVF-SAM2](https://huggingface.co/YxZhang/evf-sam2/tree/main), [huggingface/EVF-SAM](https://huggingface.co/YxZhang/evf-sam/tree/main) to ```ComfyUI/models/EVF-SAM``` folder(save the models in their respective subdirectories). diff --git a/README_CN.MD b/README_CN.MD index 2225300..4bfc722 100644 --- a/README_CN.MD +++ b/README_CN.MD @@ -105,6 +105,10 @@ git clone https://github.com/chflame163/ComfyUI_LayerStyle.git ## 更新说明 **如果本插件更新后出现依赖包错误，请双击运行插件目录下的```install_requirements.bat```(官方便携包)，或 ```install_requirements_aki.bat```(秋叶整合包) 重新安装依赖包。 +* 添加 [SAM2Ultra](#SAM2Ultra), [SAM2VideoUltra](#SAM2VideoUltra), [ObjectDetectorFL2](#ObjectDetectorFL2), [ObjectDetectorYOLOWorld](#ObjectDetectorYOLOWorld), [ObjectDetectorYOLO8](#ObjectDetectorYOLO8), [ObjectDetectorMask](#ObjectDetectorMask) 和 [BBoxJoin](#BBoxJoin)节点。 +请从[百度网盘](https://pan.baidu.com/s/1xaQYBA6ktxvAxm310HXweQ?pwd=auki) 或者 [huggingface.co/Kijai/sam2-safetensors](https://huggingface.co/Kijai/sam2-safetensors/tree/main)下载全部模型文件并复制到```ComfyUI/models/sam2```文件夹; +从 [百度网盘](https://pan.baidu.com/s/1QpjajeTA37vEAU2OQnbDcQ?pwd=nqsk) 或[GoogleDrive](https://drive.google.com/drive/folders/1nrsfq4S-yk9ewJgwrhXAoNVqIFLZ1at7?usp=sharing)下载模型文件并复制到```ComfyUI/models/yolo-world```文件夹。 +本次更新引入了新的依赖，请重新安装依赖包。 * 添加 [RandomGenerator](#RandomGenerator) 节点，用于生成指定范围内的随机数，有int，float，bool输出，支持按图片批量生成不同的随机数。 * 添加 [EVF-SAMUltra](#EVFSAMUltra) 节点，是[EVF-SAM](https://github.com/hustvl/EVF-SAM)在ComfyUI中的实现。请从[百度网盘](https://pan.baidu.com/s/1EvaxgKcCxUpMbYKzLnEx9w?pwd=69bn) 或者 [huggingface/EVF-SAM2](https://huggingface.co/YxZhang/evf-sam2/tree/main), [huggingface/EVF-SAM](https://huggingface.co/YxZhang/evf-sam/tree/main) 下载全部模型文件并复制到```ComfyUI/models/EVF-SAM```文件夹(请将模型保存在各自子目录中)。 由于引入了新的依赖，插件升级后请重新安装依赖包。 @@ -1565,6 +1569,114 @@ SegmentAnythingUltra的V2升级版，增加了VITMatte边缘处理方法。 * device: 设置是否使用cuda。 * max_megapixels: 设置vitmatte运算的最大尺寸。 +### SAM2Ultra +本节点是[kijai/ComfyUI-segment-anything-2](https://github.com/kijai/ComfyUI-segment-anything-2)的改造版本。感谢[kijai](https://github.com/kijai)为ComfyUI社区做出的巨大贡献。 +SAM2 Ultra 节点仅支持单张图片，如果需要处理多张图片，请先将image batch 转换为 image list。 +*请从[百度网盘](https://pan.baidu.com/s/1xaQYBA6ktxvAxm310HXweQ?pwd=auki) 或者 [huggingface.co/Kijai/sam2-safetensors](https://huggingface.co/Kijai/sam2-safetensors/tree/main)下载全部模型文件并复制到```ComfyUI/models/sam2```文件夹。 + +![image](image/sam2_example.jpg) + +节点选项说明: +![image](image/sam2_ultra_node.jpg) + +* image: 图片输入。 +* bboxes: 识别框数据输入。 +* sam2_model: 选择SAM2模型。 +* presicion: 模型精度，可选择fp16, bf16 和 fp32。 +* bbox_select: 选择输入的框数据。有3个选项："all"为全部选择，"first"为选择置信度最高的框，"by_index"可以指定框的索引。 +* select_index: 当bbox_select为"by_index"时，此选项有效。0为第一张。可以输入多个值，中间用任意非数字字符分隔，包括不仅限于逗号，句号，分号，空格或者字母，甚至中文。 +* cache_model: 是否缓存模型。缓存模型后将节省模型加载的时间。 +* detail_method: 边缘处理方法。提供了VITMatte, VITMatte(local), PyMatting, GuidedFilter。如果首次使用VITMatte后模型已经下载，之后可以使用VITMatte(local)。 +* 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。 +* 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。 +* black_point: 边缘黑色采样阈值。 +* white_point: 边缘黑色采样阈值。 +* process_detail: 此处设为False将跳过边缘处理以节省运行时间。 +* device: 设置是否使用cuda。 +* max_megapixels: 设置vitmatte运算的最大尺寸。 + +### SAM2VideoUltra +SAM2 Video Ultra 节点支持处理多张图片或视频序列帧。请在序列的第一帧定义识别框数据以保证正确识别。 + +https://github.com/user-attachments/assets/4726b8bf-9b98-4630-8f54-cb7ed7a3d2c5 + +节点选项说明: +![image](image/sam2_video_ultra_node.jpg) + +* image: 图片输入。 +* bboxes: 识别框数据输入。 +* pre_mask: 可选输入遮罩，这里的遮罩将作为关注范围限制，有助于提高识别准确度。 +* sam2_model: 选择SAM2模型。 +* presicion: 模型精度，可选择fp16, bf16 和 fp32。 +* cache_model: 是否缓存模型。缓存模型后将节省模型加载的时间。 +* individual_object: 当设置为 True时，将专注于识别单一对象。设置为False时，将尝试为多个对象生成识别框。 +* mask_preview_color: 在预览输出中显示非遮罩区域的颜色。 +* detail_method: 边缘处理方法。仅VITMatte可用。 +* 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。 +* 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。 +* black_point: 边缘黑色采样阈值。 +* white_point: 边缘黑色采样阈值。 +* process_detail: 此处设为False将跳过边缘处理以节省运行时间。 +* device: 本节点限制仅使用cuda。 +* max_megapixels: 设置vitmatte运算的最大尺寸。更大的尺寸将获得更精细的遮罩边缘，但会导致运算速度明显下降。 + +### ObjectDetectorFL2 +使用Florence2模型识别图片中的对象，并输出识别框数据。 +*请从 [百度网盘](https://pan.baidu.com/s/1hzw9-QiU1vB8pMbBgofZIA?pwd=mfl3)下载模型文件并复制到```ComfyUI/models/florence2```文件夹。 + +节点选项说明: +![image](image/object_detector_fl2_node.jpg) + +* image: 图片输入。 +* florence2_model: Florence2模型。从[Florence2模型加载器](#LoadFlorence2Model)输入。 +* prompt: 描述需要识别的对象。 +* bbox_select: 选择输入的框数据。有3个选项："all"为全部选择，"first"为选择置信度最高的框，"by_index"可以指定框的索引。 +* select_index: 当bbox_select为"by_index"时，此选项有效。0为第一张。可以输入多个值，中间用任意非数字字符分隔，包括不仅限于逗号，句号，分号，空格或者字母，甚至中文。 + +### ObjectDetectorYOLOWorld +使用YOLO World模型识别图片中的对象，并输出识别框数据。 +*请从 [百度网盘](https://pan.baidu.com/s/1QpjajeTA37vEAU2OQnbDcQ?pwd=nqsk) 或[GoogleDrive](https://drive.google.com/drive/folders/1nrsfq4S-yk9ewJgwrhXAoNVqIFLZ1at7?usp=sharing)下载模型文件并复制到```ComfyUI/models/yolo-world```文件夹。 + +节点选项说明: +![image](image/object_detector_yolo_world_node.jpg) + +* image: 图片输入。 +* confidence_threshold: 置信度阈值。 +* nms_iou_threshold: 非极大值抑制阈值。 +* prompt: 描述需要识别的对象。 +* bbox_select: 选择输入的框数据。有3个选项："all"为全部选择，"first"为选择置信度最高的框，"by_index"可以指定框的索引。 +* select_index: 当bbox_select为"by_index"时，此选项有效。0为第一张。可以输入多个值，中间用任意非数字字符分隔，包括不仅限于逗号，句号，分号，空格或者字母，甚至中文。 + +### ObjectDetectorYOLO8 +使用YOLO 8模型识别图片中的对象，并输出识别框数据。 +*请在 [GoogleDrive](https://drive.google.com/drive/folders/1I5TISO2G1ArSkKJu1O9b4Uvj3DVgn5d2) 或者 [百度网盘](https://pan.baidu.com/s/1pEY6sjABQaPs6QtpK0q6XA?pwd=grqe) 下载模型文件并放到 ```ComfyUI/models/yolo``` 文件夹。 + +节点选项说明: +![image](image/object_detector_yolo8_node.jpg) +* image: 图片输入。 +* yolo_model: 选择yolo模型。 +* bbox_select: 选择输入的框数据。有3个选项："all"为全部选择，"first"为选择置信度最高的框，"by_index"可以指定框的索引。 +* select_index: 当bbox_select为"by_index"时，此选项有效。0为第一张。可以输入多个值，中间用任意非数字字符分隔，包括不仅限于逗号，句号，分号，空格或者字母，甚至中文。 + +### ObjectDetectorMask +使用遮罩作为识别框数据。遮罩上所有被白色区域包围的区域，将被识别为一个对象。多个封闭区域将各自识别。 + +节点选项说明: +![image](image/object_detector_mask_node.jpg) +* object_mask: 遮罩输入。 +* bbox_select: 选择输入的框数据。有3个选项："all"为全部选择，"first"为选择置信度最高的框，"by_index"可以指定框的索引。 +* select_index: 当bbox_select为"by_index"时，此选项有效。0为第一张。可以输入多个值，中间用任意非数字字符分隔，包括不仅限于逗号，句号，分号，空格或者字母，甚至中文。 + +### BBoxJoin +合并识别框数据。 + +节点选项说明: +![image](image/bbox_join_node.jpg) +* bboxes_1: 必选输入。第一组识别框。 +* bboxes_2: 可选输入。第二组识别框。 +* bboxes_3: 可选输入。第三组识别框。 +* bboxes_4: 可选输入。第四组识别框。 + ### EVF-SAMUltra 本节点是[EVF-SAM](https://github.com/hustvl/EVF-SAM)在ComfyUI中的实现。 *请从[百度网盘](https://pan.baidu.com/s/1EvaxgKcCxUpMbYKzLnEx9w?pwd=69bn) 或者 [huggingface/EVF-SAM2](https://huggingface.co/YxZhang/evf-sam2/tree/main), [huggingface/EVF-SAM](https://huggingface.co/YxZhang/evf-sam/tree/main) 下载全部模型文件并复制到```ComfyUI/models/EVF-SAM```文件夹(请将模型保存在各自子目录中)。 diff --git a/image/bbox_join_node.jpg b/image/bbox_join_node.jpg new file mode 100644 index 0000000..c80cbb1 Binary files /dev/null and b/image/bbox_join_node.jpg differ diff --git a/image/object_detector_fl2_node.jpg b/image/object_detector_fl2_node.jpg new file mode 100644 index 0000000..ceae99f Binary files /dev/null and b/image/object_detector_fl2_node.jpg differ diff --git a/image/object_detector_mask_node.jpg b/image/object_detector_mask_node.jpg new file mode 100644 index 0000000..e2e3fbd Binary files /dev/null and b/image/object_detector_mask_node.jpg differ diff --git a/image/object_detector_yolo8_node.jpg b/image/object_detector_yolo8_node.jpg new file mode 100644 index 0000000..000ecfb Binary files /dev/null and b/image/object_detector_yolo8_node.jpg differ diff --git a/image/object_detector_yolo_world_node.jpg b/image/object_detector_yolo_world_node.jpg new file mode 100644 index 0000000..22b9f9f Binary files /dev/null and b/image/object_detector_yolo_world_node.jpg differ diff --git a/image/sam2_example.jpg b/image/sam2_example.jpg new file mode 100644 index 0000000..f8d1d61 Binary files /dev/null and b/image/sam2_example.jpg differ diff --git a/image/sam2_ultra_node.jpg b/image/sam2_ultra_node.jpg new file mode 100644 index 0000000..0dfbaf8 Binary files /dev/null and b/image/sam2_ultra_node.jpg differ diff --git a/image/sam2_video_ultra_node.jpg b/image/sam2_video_ultra_node.jpg new file mode 100644 index 0000000..383a6c1 Binary files /dev/null and b/image/sam2_video_ultra_node.jpg differ diff --git a/py/florence2_ultra.py b/py/florence2_ultra.py index 7e6a64f..2dfa4ca 100644 --- a/py/florence2_ultra.py +++ b/py/florence2_ultra.py @@ -12,7 +12,7 @@ colormap = ['blue', 'orange', 'green', 'purple', 'brown', 'pink', 'gray', 'olive device = comfy.model_management.get_torch_device() -model_repos = { +fl2_model_repos = { "base": "microsoft/Florence-2-base", "base-ft": "microsoft/Florence-2-base-ft", "large": "microsoft/Florence-2-large", @@ -24,6 +24,7 @@ model_repos = { "CogFlorence-2.1-Large": "thwri/CogFlorence-2.1-Large" } + def fixed_get_imports(filename) -> list[str]: """Workaround for FlashAttention""" if os.path.basename(filename) != "modeling_florence2.py": @@ -41,7 +42,7 @@ def load_model(version): if not os.path.exists(model_path): log(f"Downloading Florence2 {version} model...") - repo_id = model_repos[version] + repo_id = fl2_model_repos[version] from huggingface_hub import snapshot_download snapshot_download(repo_id=repo_id, local_dir=model_path, ignore_patterns=["*.md", "*.txt"]) @@ -60,6 +61,7 @@ def load_model(version): return (model.to(device), processor) + def fig_to_pil(fig): buf = io.BytesIO() fig.savefig(buf, format='png', dpi=100, bbox_inches='tight', pad_inches=0) @@ -367,7 +369,7 @@ class LS_LoadFlorence2Model: @classmethod def INPUT_TYPES(s): - model_list = list(model_repos.keys()) + model_list = list(fl2_model_repos.keys()) return { "required": { "version": (model_list,{"default": model_list[0]}), diff --git a/py/imagefunc.py b/py/imagefunc.py index 93eb8f4..cc8906c 100644 --- a/py/imagefunc.py +++ b/py/imagefunc.py @@ -33,6 +33,7 @@ import torch.nn.functional as F from transformers import AutoModel, AutoProcessor, StoppingCriteria, StoppingCriteriaList, AutoModelForCausalLM from colorsys import rgb_to_hsv import folder_paths +import comfy.model_management from .filmgrainer import processing as processing_utils from .blendmodes import * @@ -156,6 +157,25 @@ def mask2image(mask:torch.Tensor) -> Image: '''Image Functions''' +def draw_bounding_boxes(image:Image, bboxes:list, color:str="#FF0000", line_width:int=5) -> Image: + """ + Draw bounding boxes on the image using the coordinates provided in the bboxes dictionary. + """ + if len(bboxes) > 0: + draw = ImageDraw.Draw(image) + width, height = image.size + if line_width < 0: #auto line width + line_width = (image.width + image.height) // 300 + + for box in bboxes: + xmin = min(box[0],box[2]) + xmax = max(box[0],box[2]) + ymin = min(box[1],box[3]) + ymax = max(box[1], box[3]) + draw.rectangle([xmin, ymin, xmax, ymax], outline=color, width=line_width) + + return image + # 颜色加深 def blend_color_burn(background_image:Image, layer_image:Image) -> Image: img_1 = cv22ski(pil2cv2(background_image)) @@ -1491,7 +1511,7 @@ def generate_VITMatte(image:Image, trimap:Image, local_files_only:bool=False, de if width * height > max_megapixels: image = image.resize((target_width, target_height), Image.BILINEAR) trimap = trimap.resize((target_width, target_height), Image.BILINEAR) - log(f"vitmatte image size {width}x{height} too large, resize to {target_width}x{target_height} for processing.") + # log(f"vitmatte image size {width}x{height} too large, resize to {target_width}x{target_height} for processing.") model_name = "hustvl/vitmatte-small-composition-1k" if device=="cpu": device = torch.device('cpu') @@ -1503,7 +1523,7 @@ def generate_VITMatte(image:Image, trimap:Image, local_files_only:bool=False, de device = torch.device('cpu') vit_matte_model = load_VITMatte_model(model_name=model_name, local_files_only=local_files_only) vit_matte_model.model.to(device) - log(f"vitmatte processing, image size = {image.width}x{image.height}, device = {device}.") + # log(f"vitmatte processing, image size = {image.width}x{image.height}, device = {device}.") inputs = vit_matte_model.processor(images=image, trimaps=trimap, return_tensors="pt") with torch.no_grad(): inputs = {k: v.to(device) for k, v in inputs.items()} @@ -2104,7 +2124,18 @@ chop_mode = [ chop_mode_v2 = list(BLEND_MODES.keys()) -'''Load INI File''' +'''Load File''' + + +def get_files(model_path: str, file_ext_list:list) -> dict: + file_list = [] + for ext in file_ext_list: + file_list.extend(glob.glob(os.path.join(model_path, '*' + ext))) + files_dict = {} + for i in range(len(file_list)): + _, filename = os.path.split(file_list[i]) + files_dict[filename] = file_list[i] + return files_dict default_lut_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.normpath(__file__))), 'lut') default_font_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.normpath(__file__))), 'font') @@ -2202,6 +2233,8 @@ for i in range(len(__font_file_list)): FONT_LIST = list(FONT_DICT.keys()) log(f'Find {len(FONT_LIST)} Fonts in {default_font_dir}') + + gemini_generate_config = { "temperature": 0, "top_p": 1, diff --git a/py/object_detector.py b/py/object_detector.py new file mode 100644 index 0000000..7010065 --- /dev/null +++ b/py/object_detector.py @@ -0,0 +1,357 @@ + +from .imagefunc import * + +select_list = ["all", "first", "by_index"] + + +def select_bboxes(bboxes:list, bbox_select:str, select_index:str) -> list: + indexs = extract_numbers(select_index) + if bbox_select == "all": + return bboxes + elif bbox_select == "first": + return [bboxes[0]] + elif bbox_select == "by_index": + new_bboxes = [] + for i in indexs: + try: + new_bboxes.append(bboxes[i]) + except IndexError: + log(f"Object detector output by_index: invalid bbox index {i}", message_type='warning') + return new_bboxes + + +class LS_BBOXES_JOIN: + + def __init__(self): + self.NODE_NAME = 'BBoxes Join' + + @classmethod + def INPUT_TYPES(cls): + + return { + "required": { + "bboxes_1": ("BBOXES",), + }, + "optional": { + "bboxes_2": ("BBOXES",), + "bboxes_3": ("BBOXES",), + "bboxes_4": ("BBOXES",), + } + } + + RETURN_TYPES = ("BBOXES",) + RETURN_NAMES = ("bboxes",) + FUNCTION = 'bboxes_join' + CATEGORY = '😺dzNodes/LayerMask' + + def bboxes_join(self, bboxes_1, bboxes_2=None, bboxes_3=None, bboxes_4=None): + if bboxes_2 is not None: + bboxes_1.extend(bboxes_2) + if bboxes_3 is not None: + bboxes_1.extend(bboxes_3) + if bboxes_4 is not None: + bboxes_1.extend(bboxes_4) + return (bboxes_1,) + +class LS_OBJECT_DETECTOR_FL2: + + def __init__(self): + self.NODE_NAME = 'Object Detector Florence2' + + @classmethod + def INPUT_TYPES(cls): + + return { + "required": { + "image": ("IMAGE", ), # + "prompt": ("STRING", {"default": "subject"}), + "florence2_model": ("FLORENCE2",), + "bbox_select": (select_list,), + "select_index": ("STRING", {"default": "0,"},), + }, + "optional": { + } + } + + RETURN_TYPES = ("BBOXES", "IMAGE",) + RETURN_NAMES = ("bboxes", "preview",) + FUNCTION = 'object_detector_fl2' + CATEGORY = '😺dzNodes/LayerMask' + + def object_detector_fl2(self, image, prompt, florence2_model, bbox_select, select_index): + + bboxes = [] + ret_previews = [] + max_new_tokens = 512 + num_beams = 3 + do_sample = False + fill_mask = False + + model = florence2_model['model'] + processor = florence2_model['processor'] + + img = tensor2pil(image[0]).convert("RGB") + task = 'caption to phrase grounding' + from .florence2_ultra import process_image + results, _ = process_image(model, processor, img, task, + max_new_tokens, num_beams, do_sample, + fill_mask, prompt) + + if isinstance(results, dict): + results["width"] = img.width + results["height"] = img.height + + bboxes = self.fbboxes_to_list(results) + bboxes = select_bboxes(bboxes, bbox_select, select_index) + preview = draw_bounding_boxes(img, bboxes, color="#FF0000", line_width=-1) + ret_previews.append(pil2tensor(preview)) + if len(bboxes) == 0: + log(f"{self.NODE_NAME} no object found", message_type='warning') + else: + log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info') + return (bboxes, torch.cat(ret_previews, dim=0)) + + def fbboxes_to_list(self, F_BBOXES) -> list: + if isinstance(F_BBOXES, str): + return None + ret_bboxes = [] + width = F_BBOXES["width"] + height = F_BBOXES["height"] + x1_c = width + y1_c = height + x2_c = y2_c = 0 + label = "" + if "bboxes" in F_BBOXES: + for idx in range(len(F_BBOXES["bboxes"])): + bbox = F_BBOXES["bboxes"][idx] + new_label = F_BBOXES["labels"][idx].removeprefix("") + if new_label not in label: + if idx > 0: + label = label + ", " + label = label + new_label + if len(bbox) == 4: + x1, y1, x2, y2 = int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]) + elif len(bbox) == 8: + x1 = int(min(bbox[0::2])) + x2 = int(max(bbox[0::2])) + y1 = int(min(bbox[1::2])) + y2 = int(max(bbox[1::2])) + else: + continue + x1_c = min(x1_c, x1) + y1_c = min(y1_c, y1) + x2_c = max(x2_c, x2) + y2_c = max(y2_c, y2) + ret_bboxes.append([x1, y1, x2, y2]) + else: + x1_c = width + y1_c = height + x2_c = y2_c = 0 + for polygon in F_BBOXES["polygons"][0]: + if len(_polygon) < 3: + print('Invalid polygon:', _polygon) + continue + x1_c = min(x1_c, int(min(polygon[0::2]))) + x2_c = max(x2_c, int(max(polygon[0::2]))) + y1_c = min(y1_c, int(min(polygon[1::2]))) + y2_c = max(y2_c, int(max(polygon[1::2]))) + ret_bboxes.append(x1_c, y1_c, x2_c, y2_c) + return ret_bboxes + +class LS_OBJECT_DETECTOR_MASK: + + def __init__(self): + self.NODE_NAME = 'Object Detector MASK' + + @classmethod + def INPUT_TYPES(cls): + + return { + "required": { + "object_mask": ("MASK",), + "bbox_select": (select_list,), + "select_index": ("STRING", {"default": "0,"},), + }, + "optional": { + } + } + + RETURN_TYPES = ("BBOXES", "IMAGE",) + RETURN_NAMES = ("bboxes", "preview",) + FUNCTION = 'object_detector_mask' + CATEGORY = '😺dzNodes/LayerMask' + + def object_detector_mask(self, object_mask, bbox_select, select_index): + + bboxes = [] + if object_mask.dim() == 2: + object_mask = torch.unsqueeze(object_mask, 0) + + cv_mask = tensor2cv2(object_mask[0]) + cv_mask = cv2.cvtColor(cv_mask, cv2.COLOR_BGR2GRAY) + _, binary = cv2.threshold(cv_mask, 127, 255, cv2.THRESH_BINARY) + # invert mask + # binary = cv2.bitwise_not(binary) + contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) + for contour in contours: + x, y, w, h = cv2.boundingRect(contour) + bboxes.append([x, y, x + w, y + h]) + bboxes = select_bboxes(bboxes, bbox_select, select_index) + ret_previews = [] + preview = draw_bounding_boxes(tensor2pil(object_mask[0]).convert("RGB"), bboxes, color="#FF0000", line_width=-1) + ret_previews.append(pil2tensor(preview)) + + if len(bboxes) == 0: + log(f"{self.NODE_NAME} no object found", message_type='warning') + else: + log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info') + + return (bboxes, torch.cat(ret_previews, dim=0)) + + +class LS_OBJECT_DETECTOR_YOLO8: + + def __init__(self): + self.NODE_NAME = 'Object Detector YOLO8' + + @classmethod + def INPUT_TYPES(cls): + model_ext = [".pt"] + model_path = os.path.join(folder_paths.models_dir, 'yolo') + FILES_DICT = get_files(model_path, model_ext) + FILE_LIST = list(FILES_DICT.keys()) + return { + "required": { + "image": ("IMAGE", ), + "yolo_model": (FILE_LIST,), + "bbox_select": (select_list,), + "select_index": ("STRING", {"default": "0,"},), + }, + "optional": { + } + } + + RETURN_TYPES = ("BBOXES", "IMAGE",) + RETURN_NAMES = ("bboxes", "preview",) + FUNCTION = 'object_detector_yolo8' + CATEGORY = '😺dzNodes/LayerMask' + + def object_detector_yolo8(self, image, yolo_model, bbox_select, select_index): + + from ultralytics import YOLO + model_path = os.path.join(folder_paths.models_dir, 'yolo') + yolo_model = YOLO(os.path.join(model_path, yolo_model)) + + bboxes = [] + ret_previews = [] + + img = torch.unsqueeze(image[0], 0) + _image = tensor2pil(img) + results = yolo_model(_image, retina_masks=True) + for result in results: + yolo_plot_image = cv2.cvtColor(result.plot(), cv2.COLOR_BGR2RGB) + + # no mask, if have box, draw box + if result.boxes is not None and len(result.boxes.xyxy) > 0: + for box in result.boxes: + x1, y1, x2, y2 = box.xyxy[0].cpu().numpy() + bboxes.append([x1, y1, x2, y2]) + + bboxes = select_bboxes(bboxes, bbox_select, select_index) + preview = draw_bounding_boxes(_image.convert("RGB"), bboxes, color="#FF0000", line_width=-1) + ret_previews.append(pil2tensor(preview)) + + if len(bboxes) == 0: + log(f"{self.NODE_NAME} no object found", message_type='warning') + else: + log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info') + + return (bboxes, torch.cat(ret_previews, dim=0),) + +class LS_OBJECT_DETECTOR_YOLOWORLD: + + def __init__(self): + self.NODE_NAME = 'Object Detector YOLO-WORLD' + self.model_path = os.path.join(folder_paths.models_dir, 'yolo-world') + os.environ['MODEL_CACHE_DIR'] = self.model_path + + @classmethod + def INPUT_TYPES(cls): + model_list =['yolo_world/v2-x', 'yolo_world/v2-l', 'yolo_world/v2-m', + 'yolo_world/v2-s', 'yolo_world/l', 'yolo_world/m', + 'yolo_world/s'] + return { + "required": { + "image": ("IMAGE", ), + "yolo_world_model": (model_list,), + "confidence_threshold": ("FLOAT", {"default": 0.05, "min": 0, "max": 1, "step": 0.01}), + "nms_iou_threshold": ("FLOAT", {"default": 0.3, "min": 0, "max": 1, "step": 0.01}), + "prompt": ("STRING", {"default": "subject"}), + "bbox_select": (select_list,), + "select_index": ("STRING", {"default": "0,"},), + }, + "optional": { + } + } + + RETURN_TYPES = ("BBOXES", "IMAGE",) + RETURN_NAMES = ("bboxes", "preview",) + FUNCTION = 'object_detector_yoloworld' + CATEGORY = '😺dzNodes/LayerMask' + + def object_detector_yoloworld(self, image, yolo_world_model, + confidence_threshold, nms_iou_threshold, prompt, + bbox_select, select_index): + import supervision as sv + + model=self.load_yolo_world_model(yolo_world_model, prompt) + infer_outputs = [] + img = (255 * image[0].cpu().numpy()).astype(np.uint8) + results = model.infer( + img, confidence=confidence_threshold) + detections = sv.Detections.from_inference(results) + detections = detections.with_nms( + class_agnostic=False, + threshold=nms_iou_threshold + ) + infer_outputs.append(detections) + bboxes = infer_outputs[0].xyxy.tolist() + bboxes = [[int(value) for value in sublist] for sublist in bboxes] + bboxes = select_bboxes(bboxes, bbox_select, select_index) + ret_previews = [] + preview = draw_bounding_boxes(tensor2pil(image[0]).convert('RGB'), bboxes, color="#FF0000", line_width=-1) + ret_previews.append(pil2tensor(preview)) + + if len(bboxes) == 0: + log(f"{self.NODE_NAME} no object found", message_type='warning') + else: + log(f"{self.NODE_NAME} found {len(bboxes)} object(s)", message_type='info') + + return (bboxes, torch.cat(ret_previews, dim=0)) + + def process_categories(self, categories: str) -> List[str]: + return [category.strip().lower() for category in categories.split(',')] + + def load_yolo_world_model(self,model_id: str, categories: str) -> List[torch.nn.Module]: + from inference.models import YOLOWorld as YOLOWorldImpl + model = YOLOWorldImpl(model_id=model_id) + categories = self.process_categories(categories) + model.set_classes(categories) + return model + + +NODE_CLASS_MAPPINGS = { + "LayerMask: BBoxJoin": LS_BBOXES_JOIN, + "LayerMask: ObjectDetectorFL2": LS_OBJECT_DETECTOR_FL2, + "LayerMask: ObjectDetectorMask": LS_OBJECT_DETECTOR_MASK, + "LayerMask: ObjectDetectorYOLO8": LS_OBJECT_DETECTOR_YOLO8, + "LayerMask: ObjectDetectorYOLOWorld": LS_OBJECT_DETECTOR_YOLOWORLD +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LayerMask: BBoxJoin": "LayerMask: BBox Join", + "LayerMask: ObjectDetectorFL2": "LayerMask: Object Detector Florence2", + "LayerMask: ObjectDetectorMask": "LayerMask: Object Detector Mask", + "LayerMask: ObjectDetectorYOLO8": "LayerMask: Object Detector YOLO8", + "LayerMask: ObjectDetectorYOLOWorld": "LayerMask: Object Detector YOLO World" +} \ No newline at end of file diff --git a/py/sam2/__init__.py b/py/sam2/__init__.py new file mode 100644 index 0000000..5277f46 --- /dev/null +++ b/py/sam2/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/py/sam2/automatic_mask_generator.py b/py/sam2/automatic_mask_generator.py new file mode 100644 index 0000000..e589132 --- /dev/null +++ b/py/sam2/automatic_mask_generator.py @@ -0,0 +1,434 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +# Adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/automatic_mask_generator.py +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +from torchvision.ops.boxes import batched_nms, box_area # type: ignore + +from ..sam2.modeling.sam2_base import SAM2Base +from ..sam2.sam2_image_predictor import SAM2ImagePredictor +from ..sam2.utils.amg import ( + area_from_rle, + batch_iterator, + batched_mask_to_box, + box_xyxy_to_xywh, + build_all_layer_point_grids, + calculate_stability_score, + coco_encode_rle, + generate_crop_boxes, + is_box_near_crop_edge, + mask_to_rle_pytorch, + MaskData, + remove_small_regions, + rle_to_mask, + uncrop_boxes_xyxy, + uncrop_masks, + uncrop_points, +) + + +class SAM2AutomaticMaskGenerator: + def __init__( + self, + model: SAM2Base, + points_per_side: Optional[int] = 32, + points_per_batch: int = 64, + pred_iou_thresh: float = 0.8, + stability_score_thresh: float = 0.95, + stability_score_offset: float = 1.0, + mask_threshold: float = 0.0, + box_nms_thresh: float = 0.7, + crop_n_layers: int = 0, + crop_nms_thresh: float = 0.7, + crop_overlap_ratio: float = 512 / 1500, + crop_n_points_downscale_factor: int = 1, + point_grids: Optional[List[np.ndarray]] = None, + min_mask_region_area: int = 0, + output_mode: str = "binary_mask", + use_m2m: bool = False, + multimask_output: bool = True, + ) -> None: + """ + Using a SAM 2 model, generates masks for the entire image. + Generates a grid of point prompts over the image, then filters + low quality and duplicate masks. The default settings are chosen + for SAM 2 with a HieraL backbone. + + Arguments: + model (Sam): The SAM 2 model to use for mask prediction. + points_per_side (int or None): The number of points to be sampled + along one side of the image. The total number of points is + points_per_side**2. If None, 'point_grids' must provide explicit + point sampling. + points_per_batch (int): Sets the number of points run simultaneously + by the model. Higher numbers may be faster but use more GPU memory. + pred_iou_thresh (float): A filtering threshold in [0,1], using the + model's predicted mask quality. + stability_score_thresh (float): A filtering threshold in [0,1], using + the stability of the mask under changes to the cutoff used to binarize + the model's mask predictions. + stability_score_offset (float): The amount to shift the cutoff when + calculated the stability score. + mask_threshold (float): Threshold for binarizing the mask logits + box_nms_thresh (float): The box IoU cutoff used by non-maximal + suppression to filter duplicate masks. + crop_n_layers (int): If >0, mask prediction will be run again on + crops of the image. Sets the number of layers to run, where each + layer has 2**i_layer number of image crops. + crop_nms_thresh (float): The box IoU cutoff used by non-maximal + suppression to filter duplicate masks between different crops. + crop_overlap_ratio (float): Sets the degree to which crops overlap. + In the first crop layer, crops will overlap by this fraction of + the image length. Later layers with more crops scale down this overlap. + crop_n_points_downscale_factor (int): The number of points-per-side + sampled in layer n is scaled down by crop_n_points_downscale_factor**n. + point_grids (list(np.ndarray) or None): A list over explicit grids + of points used for sampling, normalized to [0,1]. The nth grid in the + list is used in the nth crop layer. Exclusive with points_per_side. + min_mask_region_area (int): If >0, postprocessing will be applied + to remove disconnected regions and holes in masks with area smaller + than min_mask_region_area. Requires opencv. + output_mode (str): The form masks are returned in. Can be 'binary_mask', + 'uncompressed_rle', or 'coco_rle'. 'coco_rle' requires pycocotools. + For large resolutions, 'binary_mask' may consume large amounts of + memory. + use_m2m (bool): Whether to add a one step refinement using previous mask predictions. + multimask_output (bool): Whether to output multimask at each point of the grid. + """ + + assert (points_per_side is None) != ( + point_grids is None + ), "Exactly one of points_per_side or point_grid must be provided." + if points_per_side is not None: + self.point_grids = build_all_layer_point_grids( + points_per_side, + crop_n_layers, + crop_n_points_downscale_factor, + ) + elif point_grids is not None: + self.point_grids = point_grids + else: + raise ValueError("Can't have both points_per_side and point_grid be None.") + + assert output_mode in [ + "binary_mask", + "uncompressed_rle", + "coco_rle", + ], f"Unknown output_mode {output_mode}." + if output_mode == "coco_rle": + try: + from pycocotools import mask as mask_utils # type: ignore # noqa: F401 + except ImportError as e: + print("Please install pycocotools") + raise e + + self.predictor = SAM2ImagePredictor( + model, + max_hole_area=min_mask_region_area, + max_sprinkle_area=min_mask_region_area, + ) + self.points_per_batch = points_per_batch + self.pred_iou_thresh = pred_iou_thresh + self.stability_score_thresh = stability_score_thresh + self.stability_score_offset = stability_score_offset + self.mask_threshold = mask_threshold + self.box_nms_thresh = box_nms_thresh + self.crop_n_layers = crop_n_layers + self.crop_nms_thresh = crop_nms_thresh + self.crop_overlap_ratio = crop_overlap_ratio + self.crop_n_points_downscale_factor = crop_n_points_downscale_factor + self.min_mask_region_area = min_mask_region_area + self.output_mode = output_mode + self.use_m2m = use_m2m + self.multimask_output = multimask_output + + @torch.no_grad() + def generate(self, image: np.ndarray) -> List[Dict[str, Any]]: + """ + Generates masks for the given image. + + Arguments: + image (np.ndarray): The image to generate masks for, in HWC uint8 format. + + Returns: + list(dict(str, any)): A list over records for masks. Each record is + a dict containing the following keys: + segmentation (dict(str, any) or np.ndarray): The mask. If + output_mode='binary_mask', is an array of shape HW. Otherwise, + is a dictionary containing the RLE. + bbox (list(float)): The box around the mask, in XYWH format. + area (int): The area in pixels of the mask. + predicted_iou (float): The model's own prediction of the mask's + quality. This is filtered by the pred_iou_thresh parameter. + point_coords (list(list(float))): The point coordinates input + to the model to generate this mask. + stability_score (float): A measure of the mask's quality. This + is filtered on using the stability_score_thresh parameter. + crop_box (list(float)): The crop of the image used to generate + the mask, given in XYWH format. + """ + + # Generate masks + mask_data = self._generate_masks(image) + + # Encode masks + if self.output_mode == "coco_rle": + mask_data["segmentations"] = [ + coco_encode_rle(rle) for rle in mask_data["rles"] + ] + elif self.output_mode == "binary_mask": + mask_data["segmentations"] = [rle_to_mask(rle) for rle in mask_data["rles"]] + else: + mask_data["segmentations"] = mask_data["rles"] + + # Write mask records + curr_anns = [] + for idx in range(len(mask_data["segmentations"])): + ann = { + "segmentation": mask_data["segmentations"][idx], + "area": area_from_rle(mask_data["rles"][idx]), + "bbox": box_xyxy_to_xywh(mask_data["boxes"][idx]).tolist(), + "predicted_iou": mask_data["iou_preds"][idx].item(), + "point_coords": [mask_data["points"][idx].tolist()], + "stability_score": mask_data["stability_score"][idx].item(), + "crop_box": box_xyxy_to_xywh(mask_data["crop_boxes"][idx]).tolist(), + } + curr_anns.append(ann) + + return curr_anns + + def _generate_masks(self, image: np.ndarray) -> MaskData: + orig_size = image.shape[:2] + crop_boxes, layer_idxs = generate_crop_boxes( + orig_size, self.crop_n_layers, self.crop_overlap_ratio + ) + + # Iterate over image crops + data = MaskData() + for crop_box, layer_idx in zip(crop_boxes, layer_idxs): + crop_data = self._process_crop(image, crop_box, layer_idx, orig_size) + data.cat(crop_data) + + # Remove duplicate masks between crops + if len(crop_boxes) > 1: + # Prefer masks from smaller crops + scores = 1 / box_area(data["crop_boxes"]) + scores = scores.to(data["boxes"].device) + keep_by_nms = batched_nms( + data["boxes"].float(), + scores, + torch.zeros_like(data["boxes"][:, 0]), # categories + iou_threshold=self.crop_nms_thresh, + ) + data.filter(keep_by_nms) + data.to_numpy() + return data + + def _process_crop( + self, + image: np.ndarray, + crop_box: List[int], + crop_layer_idx: int, + orig_size: Tuple[int, ...], + ) -> MaskData: + # Crop the image and calculate embeddings + x0, y0, x1, y1 = crop_box + cropped_im = image[y0:y1, x0:x1, :] + cropped_im_size = cropped_im.shape[:2] + self.predictor.set_image(cropped_im) + + # Get points for this crop + points_scale = np.array(cropped_im_size)[None, ::-1] + points_for_image = self.point_grids[crop_layer_idx] * points_scale + + # Generate masks for this crop in batches + data = MaskData() + for (points,) in batch_iterator(self.points_per_batch, points_for_image): + batch_data = self._process_batch( + points, cropped_im_size, crop_box, orig_size, normalize=True + ) + data.cat(batch_data) + del batch_data + self.predictor.reset_predictor() + + # Remove duplicates within this crop. + keep_by_nms = batched_nms( + data["boxes"].float(), + data["iou_preds"], + torch.zeros_like(data["boxes"][:, 0]), # categories + iou_threshold=self.box_nms_thresh, + ) + data.filter(keep_by_nms) + + # Return to the original image frame + data["boxes"] = uncrop_boxes_xyxy(data["boxes"], crop_box) + data["points"] = uncrop_points(data["points"], crop_box) + data["crop_boxes"] = torch.tensor([crop_box for _ in range(len(data["rles"]))]) + + return data + + def _process_batch( + self, + points: np.ndarray, + im_size: Tuple[int, ...], + crop_box: List[int], + orig_size: Tuple[int, ...], + normalize=False, + ) -> MaskData: + orig_h, orig_w = orig_size + + # Run model on this batch + points = torch.as_tensor(points, device=self.predictor.device) + in_points = self.predictor._transforms.transform_coords( + points, normalize=normalize, orig_hw=im_size + ) + in_labels = torch.ones( + in_points.shape[0], dtype=torch.int, device=in_points.device + ) + masks, iou_preds, low_res_masks = self.predictor._predict( + in_points[:, None, :], + in_labels[:, None], + multimask_output=self.multimask_output, + return_logits=True, + ) + + # Serialize predictions and store in MaskData + data = MaskData( + masks=masks.flatten(0, 1), + iou_preds=iou_preds.flatten(0, 1), + points=points.repeat_interleave(masks.shape[1], dim=0), + low_res_masks=low_res_masks.flatten(0, 1), + ) + del masks + + if not self.use_m2m: + # Filter by predicted IoU + if self.pred_iou_thresh > 0.0: + keep_mask = data["iou_preds"] > self.pred_iou_thresh + data.filter(keep_mask) + + # Calculate and filter by stability score + data["stability_score"] = calculate_stability_score( + data["masks"], self.mask_threshold, self.stability_score_offset + ) + if self.stability_score_thresh > 0.0: + keep_mask = data["stability_score"] >= self.stability_score_thresh + data.filter(keep_mask) + else: + # One step refinement using previous mask predictions + in_points = self.predictor._transforms.transform_coords( + data["points"], normalize=normalize, orig_hw=im_size + ) + labels = torch.ones( + in_points.shape[0], dtype=torch.int, device=in_points.device + ) + masks, ious = self.refine_with_m2m( + in_points, labels, data["low_res_masks"], self.points_per_batch + ) + data["masks"] = masks.squeeze(1) + data["iou_preds"] = ious.squeeze(1) + + if self.pred_iou_thresh > 0.0: + keep_mask = data["iou_preds"] > self.pred_iou_thresh + data.filter(keep_mask) + + data["stability_score"] = calculate_stability_score( + data["masks"], self.mask_threshold, self.stability_score_offset + ) + if self.stability_score_thresh > 0.0: + keep_mask = data["stability_score"] >= self.stability_score_thresh + data.filter(keep_mask) + + # Threshold masks and calculate boxes + data["masks"] = data["masks"] > self.mask_threshold + data["boxes"] = batched_mask_to_box(data["masks"]) + + # Filter boxes that touch crop boundaries + keep_mask = ~is_box_near_crop_edge( + data["boxes"], crop_box, [0, 0, orig_w, orig_h] + ) + if not torch.all(keep_mask): + data.filter(keep_mask) + + # Compress to RLE + data["masks"] = uncrop_masks(data["masks"], crop_box, orig_h, orig_w) + data["rles"] = mask_to_rle_pytorch(data["masks"]) + del data["masks"] + + return data + + @staticmethod + def postprocess_small_regions( + mask_data: MaskData, min_area: int, nms_thresh: float + ) -> MaskData: + """ + Removes small disconnected regions and holes in masks, then reruns + box NMS to remove any new duplicates. + + Edits mask_data in place. + + Requires open-cv as a dependency. + """ + if len(mask_data["rles"]) == 0: + return mask_data + + # Filter small disconnected regions and holes + new_masks = [] + scores = [] + for rle in mask_data["rles"]: + mask = rle_to_mask(rle) + + mask, changed = remove_small_regions(mask, min_area, mode="holes") + unchanged = not changed + mask, changed = remove_small_regions(mask, min_area, mode="islands") + unchanged = unchanged and not changed + + new_masks.append(torch.as_tensor(mask).unsqueeze(0)) + # Give score=0 to changed masks and score=1 to unchanged masks + # so NMS will prefer ones that didn't need postprocessing + scores.append(float(unchanged)) + + # Recalculate boxes and remove any new duplicates + masks = torch.cat(new_masks, dim=0) + boxes = batched_mask_to_box(masks) + keep_by_nms = batched_nms( + boxes.float(), + torch.as_tensor(scores), + torch.zeros_like(boxes[:, 0]), # categories + iou_threshold=nms_thresh, + ) + + # Only recalculate RLEs for masks that have changed + for i_mask in keep_by_nms: + if scores[i_mask] == 0.0: + mask_torch = masks[i_mask].unsqueeze(0) + mask_data["rles"][i_mask] = mask_to_rle_pytorch(mask_torch)[0] + mask_data["boxes"][i_mask] = boxes[i_mask] # update res directly + mask_data.filter(keep_by_nms) + + return mask_data + + def refine_with_m2m(self, points, point_labels, low_res_masks, points_per_batch): + new_masks = [] + new_iou_preds = [] + + for cur_points, cur_point_labels, low_res_mask in batch_iterator( + points_per_batch, points, point_labels, low_res_masks + ): + best_masks, best_iou_preds, _ = self.predictor._predict( + cur_points[:, None, :], + cur_point_labels[:, None], + mask_input=low_res_mask[:, None, :], + multimask_output=False, + return_logits=True, + ) + new_masks.append(best_masks) + new_iou_preds.append(best_iou_preds) + masks = torch.cat(new_masks, dim=0) + return masks, torch.cat(new_iou_preds, dim=0) diff --git a/py/sam2/modeling/__init__.py b/py/sam2/modeling/__init__.py new file mode 100644 index 0000000..5277f46 --- /dev/null +++ b/py/sam2/modeling/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/py/sam2/modeling/backbones/__init__.py b/py/sam2/modeling/backbones/__init__.py new file mode 100644 index 0000000..5277f46 --- /dev/null +++ b/py/sam2/modeling/backbones/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/py/sam2/modeling/backbones/hieradet.py b/py/sam2/modeling/backbones/hieradet.py new file mode 100644 index 0000000..601817c --- /dev/null +++ b/py/sam2/modeling/backbones/hieradet.py @@ -0,0 +1,295 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from functools import partial +from typing import List, Tuple, Union + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ....sam2.modeling.backbones.utils import ( + PatchEmbed, + window_partition, + window_unpartition, +) + +from ....sam2.modeling.sam2_utils import DropPath, MLP + + +def do_pool(x: torch.Tensor, pool: nn.Module, norm: nn.Module = None) -> torch.Tensor: + if pool is None: + return x + # (B, H, W, C) -> (B, C, H, W) + x = x.permute(0, 3, 1, 2) + x = pool(x) + # (B, C, H', W') -> (B, H', W', C) + x = x.permute(0, 2, 3, 1) + if norm: + x = norm(x) + + return x + + +class MultiScaleAttention(nn.Module): + def __init__( + self, + dim: int, + dim_out: int, + num_heads: int, + q_pool: nn.Module = None, + ): + super().__init__() + + self.dim = dim + self.dim_out = dim_out + + self.num_heads = num_heads + head_dim = dim_out // num_heads + self.scale = head_dim**-0.5 + + self.q_pool = q_pool + self.qkv = nn.Linear(dim, dim_out * 3) + self.proj = nn.Linear(dim_out, dim_out) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + B, H, W, _ = x.shape + # qkv with shape (B, H * W, 3, nHead, C) + qkv = self.qkv(x).reshape(B, H * W, 3, self.num_heads, -1) + # q, k, v with shape (B, H * W, nheads, C) + q, k, v = torch.unbind(qkv, 2) + + # Q pooling (for downsample at stage changes) + if self.q_pool: + q = do_pool(q.reshape(B, H, W, -1), self.q_pool) + H, W = q.shape[1:3] # downsampled shape + q = q.reshape(B, H * W, self.num_heads, -1) + + # Torch's SDPA expects [B, nheads, H*W, C] so we transpose + x = F.scaled_dot_product_attention( + q.transpose(1, 2), + k.transpose(1, 2), + v.transpose(1, 2), + ) + # Transpose back + x = x.transpose(1, 2) + x = x.reshape(B, H, W, -1) + + x = self.proj(x) + + return x + + +class MultiScaleBlock(nn.Module): + def __init__( + self, + dim: int, + dim_out: int, + num_heads: int, + mlp_ratio: float = 4.0, + drop_path: float = 0.0, + norm_layer: Union[nn.Module, str] = "LayerNorm", + q_stride: Tuple[int, int] = None, + act_layer: nn.Module = nn.GELU, + window_size: int = 0, + ): + super().__init__() + + if isinstance(norm_layer, str): + norm_layer = partial(getattr(nn, norm_layer), eps=1e-6) + + self.dim = dim + self.dim_out = dim_out + self.norm1 = norm_layer(dim) + + self.window_size = window_size + + self.pool, self.q_stride = None, q_stride + if self.q_stride: + self.pool = nn.MaxPool2d( + kernel_size=q_stride, stride=q_stride, ceil_mode=False + ) + + self.attn = MultiScaleAttention( + dim, + dim_out, + num_heads=num_heads, + q_pool=self.pool, + ) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + self.norm2 = norm_layer(dim_out) + self.mlp = MLP( + dim_out, + int(dim_out * mlp_ratio), + dim_out, + num_layers=2, + activation=act_layer, + ) + + if dim != dim_out: + self.proj = nn.Linear(dim, dim_out) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + shortcut = x # B, H, W, C + x = self.norm1(x) + + # Skip connection + if self.dim != self.dim_out: + shortcut = do_pool(self.proj(x), self.pool) + + # Window partition + window_size = self.window_size + if window_size > 0: + H, W = x.shape[1], x.shape[2] + x, pad_hw = window_partition(x, window_size) + + # Window Attention + Q Pooling (if stage change) + x = self.attn(x) + if self.q_stride: + # Shapes have changed due to Q pooling + window_size = self.window_size // self.q_stride[0] + H, W = shortcut.shape[1:3] + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + pad_hw = (H + pad_h, W + pad_w) + + # Reverse window partition + if self.window_size > 0: + x = window_unpartition(x, window_size, pad_hw, (H, W)) + + x = shortcut + self.drop_path(x) + # MLP + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class Hiera(nn.Module): + """ + Reference: https://arxiv.org/abs/2306.00989 + """ + + def __init__( + self, + embed_dim: int = 96, # initial embed dim + num_heads: int = 1, # initial number of heads + drop_path_rate: float = 0.0, # stochastic depth + q_pool: int = 3, # number of q_pool stages + q_stride: Tuple[int, int] = (2, 2), # downsample stride bet. stages + stages: Tuple[int, ...] = (2, 3, 16, 3), # blocks per stage + dim_mul: float = 2.0, # dim_mul factor at stage shift + head_mul: float = 2.0, # head_mul factor at stage shift + window_pos_embed_bkg_spatial_size: Tuple[int, int] = (14, 14), + # window size per stage, when not using global att. + window_spec: Tuple[int, ...] = ( + 8, + 4, + 14, + 7, + ), + # global attn in these blocks + global_att_blocks: Tuple[int, ...] = ( + 12, + 16, + 20, + ), + return_interm_layers=True, # return feats from every stage + ): + super().__init__() + + assert len(stages) == len(window_spec) + self.window_spec = window_spec + + depth = sum(stages) + self.q_stride = q_stride + self.stage_ends = [sum(stages[:i]) - 1 for i in range(1, len(stages) + 1)] + assert 0 <= q_pool <= len(self.stage_ends[:-1]) + self.q_pool_blocks = [x + 1 for x in self.stage_ends[:-1]][:q_pool] + self.return_interm_layers = return_interm_layers + + self.patch_embed = PatchEmbed( + embed_dim=embed_dim, + ) + # Which blocks have global att? + self.global_att_blocks = global_att_blocks + + # Windowed positional embedding (https://arxiv.org/abs/2311.05613) + self.window_pos_embed_bkg_spatial_size = window_pos_embed_bkg_spatial_size + self.pos_embed = nn.Parameter( + torch.zeros(1, embed_dim, *self.window_pos_embed_bkg_spatial_size) + ) + self.pos_embed_window = nn.Parameter( + torch.zeros(1, embed_dim, self.window_spec[0], self.window_spec[0]) + ) + + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + + cur_stage = 1 + self.blocks = nn.ModuleList() + + for i in range(depth): + dim_out = embed_dim + # lags by a block, so first block of + # next stage uses an initial window size + # of previous stage and final window size of current stage + window_size = self.window_spec[cur_stage - 1] + + if self.global_att_blocks is not None: + window_size = 0 if i in self.global_att_blocks else window_size + + if i - 1 in self.stage_ends: + dim_out = int(embed_dim * dim_mul) + num_heads = int(num_heads * head_mul) + cur_stage += 1 + + block = MultiScaleBlock( + dim=embed_dim, + dim_out=dim_out, + num_heads=num_heads, + drop_path=dpr[i], + q_stride=self.q_stride if i in self.q_pool_blocks else None, + window_size=window_size, + ) + + embed_dim = dim_out + self.blocks.append(block) + + self.channel_list = ( + [self.blocks[i].dim_out for i in self.stage_ends[::-1]] + if return_interm_layers + else [self.blocks[-1].dim_out] + ) + + def _get_pos_embed(self, hw: Tuple[int, int]) -> torch.Tensor: + h, w = hw + window_embed = self.pos_embed_window + pos_embed = F.interpolate(self.pos_embed, size=(h, w), mode="bicubic") + pos_embed = pos_embed + window_embed.tile( + [x // y for x, y in zip(pos_embed.shape, window_embed.shape)] + ) + pos_embed = pos_embed.permute(0, 2, 3, 1) + return pos_embed + + def forward(self, x: torch.Tensor) -> List[torch.Tensor]: + x = self.patch_embed(x) + # x: (B, H, W, C) + + # Add pos embed + x = x + self._get_pos_embed(x.shape[1:3]) + + outputs = [] + for i, blk in enumerate(self.blocks): + x = blk(x) + if (i == self.stage_ends[-1]) or ( + i in self.stage_ends and self.return_interm_layers + ): + feats = x.permute(0, 3, 1, 2) + outputs.append(feats) + + return outputs diff --git a/py/sam2/modeling/backbones/image_encoder.py b/py/sam2/modeling/backbones/image_encoder.py new file mode 100644 index 0000000..5f92baf --- /dev/null +++ b/py/sam2/modeling/backbones/image_encoder.py @@ -0,0 +1,133 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class ImageEncoder(nn.Module): + def __init__( + self, + trunk: nn.Module, + neck: nn.Module, + scalp: int = 0, + ): + super().__init__() + self.trunk = trunk + self.neck = neck + self.scalp = scalp + assert ( + self.trunk.channel_list == self.neck.backbone_channel_list + ), f"Channel dims of trunk and neck do not match. Trunk: {self.trunk.channel_list}, neck: {self.neck.backbone_channel_list}" + + def forward(self, sample: torch.Tensor): + # Forward through backbone + features, pos = self.neck(self.trunk(sample)) + if self.scalp > 0: + # Discard the lowest resolution features + features, pos = features[: -self.scalp], pos[: -self.scalp] + + src = features[-1] + output = { + "vision_features": src, + "vision_pos_enc": pos, + "backbone_fpn": features, + } + return output + + +class FpnNeck(nn.Module): + """ + A modified variant of Feature Pyramid Network (FPN) neck + (we remove output conv and also do bicubic interpolation similar to ViT + pos embed interpolation) + """ + + def __init__( + self, + position_encoding: nn.Module, + d_model: int, + backbone_channel_list: List[int], + kernel_size: int = 1, + stride: int = 1, + padding: int = 0, + fpn_interp_model: str = "bilinear", + fuse_type: str = "sum", + fpn_top_down_levels: Optional[List[int]] = None, + ): + """Initialize the neck + :param trunk: the backbone + :param position_encoding: the positional encoding to use + :param d_model: the dimension of the model + :param neck_norm: the normalization to use + """ + super().__init__() + self.position_encoding = position_encoding + self.convs = nn.ModuleList() + self.backbone_channel_list = backbone_channel_list + for dim in backbone_channel_list: + current = nn.Sequential() + current.add_module( + "conv", + nn.Conv2d( + in_channels=dim, + out_channels=d_model, + kernel_size=kernel_size, + stride=stride, + padding=padding, + ), + ) + + self.convs.append(current) + self.fpn_interp_model = fpn_interp_model + assert fuse_type in ["sum", "avg"] + self.fuse_type = fuse_type + + # levels to have top-down features in its outputs + # e.g. if fpn_top_down_levels is [2, 3], then only outputs of level 2 and 3 + # have top-down propagation, while outputs of level 0 and level 1 have only + # lateral features from the same backbone level. + if fpn_top_down_levels is None: + # default is to have top-down features on all levels + fpn_top_down_levels = range(len(self.convs)) + self.fpn_top_down_levels = list(fpn_top_down_levels) + + def forward(self, xs: List[torch.Tensor]): + + out = [None] * len(self.convs) + pos = [None] * len(self.convs) + assert len(xs) == len(self.convs) + # fpn forward pass + # see https://github.com/facebookresearch/detectron2/blob/main/detectron2/modeling/backbone/fpn.py + prev_features = None + # forward in top-down order (from low to high resolution) + n = len(self.convs) - 1 + for i in range(n, -1, -1): + x = xs[i] + lateral_features = self.convs[n - i](x) + if i in self.fpn_top_down_levels and prev_features is not None: + top_down_features = F.interpolate( + prev_features.to(dtype=torch.float32), + scale_factor=2.0, + mode=self.fpn_interp_model, + align_corners=( + None if self.fpn_interp_model == "nearest" else False + ), + antialias=False, + ) + prev_features = lateral_features + top_down_features + if self.fuse_type == "avg": + prev_features /= 2 + else: + prev_features = lateral_features + x_out = prev_features + out[i] = x_out + pos[i] = self.position_encoding(x_out).to(x_out.dtype) + + return out, pos diff --git a/py/sam2/modeling/backbones/utils.py b/py/sam2/modeling/backbones/utils.py new file mode 100644 index 0000000..32d55c7 --- /dev/null +++ b/py/sam2/modeling/backbones/utils.py @@ -0,0 +1,95 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +"""Some utilities for backbones, in particular for windowing""" + +from typing import Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def window_partition(x, window_size): + """ + Partition into non-overlapping windows with padding if needed. + Args: + x (tensor): input tokens with [B, H, W, C]. + window_size (int): window size. + Returns: + windows: windows after partition with [B * num_windows, window_size, window_size, C]. + (Hp, Wp): padded height and width before partition + """ + B, H, W, C = x.shape + + pad_h = (window_size - H % window_size) % window_size + pad_w = (window_size - W % window_size) % window_size + if pad_h > 0 or pad_w > 0: + x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h)) + Hp, Wp = H + pad_h, W + pad_w + + x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows, (Hp, Wp) + + +def window_unpartition(windows, window_size, pad_hw, hw): + """ + Window unpartition into original sequences and removing padding. + Args: + x (tensor): input tokens with [B * num_windows, window_size, window_size, C]. + window_size (int): window size. + pad_hw (Tuple): padded height and width (Hp, Wp). + hw (Tuple): original height and width (H, W) before padding. + Returns: + x: unpartitioned sequences with [B, H, W, C]. + """ + Hp, Wp = pad_hw + H, W = hw + B = windows.shape[0] // (Hp * Wp // window_size // window_size) + x = windows.view( + B, Hp // window_size, Wp // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1) + + if Hp > H or Wp > W: + x = x[:, :H, :W, :].contiguous() + return x + + +class PatchEmbed(nn.Module): + """ + Image to Patch Embedding. + """ + + def __init__( + self, + kernel_size: Tuple[int, ...] = (7, 7), + stride: Tuple[int, ...] = (4, 4), + padding: Tuple[int, ...] = (3, 3), + in_chans: int = 3, + embed_dim: int = 768, + ): + """ + Args: + kernel_size (Tuple): kernel size of the projection layer. + stride (Tuple): stride of the projection layer. + padding (Tuple): padding size of the projection layer. + in_chans (int): Number of input image channels. + embed_dim (int): embed_dim (int): Patch embedding dimension. + """ + super().__init__() + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=kernel_size, stride=stride, padding=padding + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + x = self.proj(x) + # B C H W -> B H W C + x = x.permute(0, 2, 3, 1) + return x diff --git a/py/sam2/modeling/memory_attention.py b/py/sam2/modeling/memory_attention.py new file mode 100644 index 0000000..07788e5 --- /dev/null +++ b/py/sam2/modeling/memory_attention.py @@ -0,0 +1,169 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional + +import torch +from torch import nn, Tensor + +from ...sam2.modeling.sam.transformer import RoPEAttention + +from ...sam2.modeling.sam2_utils import get_activation_fn, get_clones + + +class MemoryAttentionLayer(nn.Module): + + def __init__( + self, + activation: str, + cross_attention: nn.Module, + d_model: int, + dim_feedforward: int, + dropout: float, + pos_enc_at_attn: bool, + pos_enc_at_cross_attn_keys: bool, + pos_enc_at_cross_attn_queries: bool, + self_attention: nn.Module, + ): + super().__init__() + self.d_model = d_model + self.dim_feedforward = dim_feedforward + self.dropout_value = dropout + self.self_attn = self_attention + self.cross_attn_image = cross_attention + + # Implementation of Feedforward model + self.linear1 = nn.Linear(d_model, dim_feedforward) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_feedforward, d_model) + + self.norm1 = nn.LayerNorm(d_model) + self.norm2 = nn.LayerNorm(d_model) + self.norm3 = nn.LayerNorm(d_model) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + self.dropout3 = nn.Dropout(dropout) + + self.activation_str = activation + self.activation = get_activation_fn(activation) + + # Where to add pos enc + self.pos_enc_at_attn = pos_enc_at_attn + self.pos_enc_at_cross_attn_queries = pos_enc_at_cross_attn_queries + self.pos_enc_at_cross_attn_keys = pos_enc_at_cross_attn_keys + + def _forward_sa(self, tgt, query_pos): + # Self-Attention + tgt2 = self.norm1(tgt) + q = k = tgt2 + query_pos if self.pos_enc_at_attn else tgt2 + tgt2 = self.self_attn(q, k, v=tgt2) + tgt = tgt + self.dropout1(tgt2) + return tgt + + def _forward_ca(self, tgt, memory, query_pos, pos, num_k_exclude_rope=0): + kwds = {} + if num_k_exclude_rope > 0: + assert isinstance(self.cross_attn_image, RoPEAttention) + kwds = {"num_k_exclude_rope": num_k_exclude_rope} + + # Cross-Attention + tgt2 = self.norm2(tgt) + tgt2 = self.cross_attn_image( + q=tgt2 + query_pos if self.pos_enc_at_cross_attn_queries else tgt2, + k=memory + pos if self.pos_enc_at_cross_attn_keys else memory, + v=memory, + **kwds, + ) + tgt = tgt + self.dropout2(tgt2) + return tgt + + def forward( + self, + tgt, + memory, + pos: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + num_k_exclude_rope: int = 0, + ) -> torch.Tensor: + + # Self-Attn, Cross-Attn + tgt = self._forward_sa(tgt, query_pos) + tgt = self._forward_ca(tgt, memory, query_pos, pos, num_k_exclude_rope) + # MLP + tgt2 = self.norm3(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout3(tgt2) + return tgt + + +class MemoryAttention(nn.Module): + def __init__( + self, + d_model: int, + pos_enc_at_input: bool, + layer: nn.Module, + num_layers: int, + batch_first: bool = True, # Do layers expect batch first input? + ): + super().__init__() + self.d_model = d_model + self.layers = get_clones(layer, num_layers) + self.num_layers = num_layers + self.norm = nn.LayerNorm(d_model) + self.pos_enc_at_input = pos_enc_at_input + self.batch_first = batch_first + + def forward( + self, + curr: torch.Tensor, # self-attention inputs + memory: torch.Tensor, # cross-attention inputs + curr_pos: Optional[Tensor] = None, # pos_enc for self-attention inputs + memory_pos: Optional[Tensor] = None, # pos_enc for cross-attention inputs + num_obj_ptr_tokens: int = 0, # number of object pointer *tokens* + ): + if isinstance(curr, list): + assert isinstance(curr_pos, list) + assert len(curr) == len(curr_pos) == 1 + curr, curr_pos = ( + curr[0], + curr_pos[0], + ) + + assert ( + curr.shape[1] == memory.shape[1] + ), "Batch size must be the same for curr and memory" + + output = curr + if self.pos_enc_at_input and curr_pos is not None: + output = output + 0.1 * curr_pos + + if self.batch_first: + # Convert to batch first + output = output.transpose(0, 1) + curr_pos = curr_pos.transpose(0, 1) + memory = memory.transpose(0, 1) + memory_pos = memory_pos.transpose(0, 1) + + for layer in self.layers: + kwds = {} + if isinstance(layer.cross_attn_image, RoPEAttention): + kwds = {"num_k_exclude_rope": num_obj_ptr_tokens} + + output = layer( + tgt=output, + memory=memory, + pos=memory_pos, + query_pos=curr_pos, + **kwds, + ) + normed_output = self.norm(output) + + if self.batch_first: + # Convert back to seq first + normed_output = normed_output.transpose(0, 1) + curr_pos = curr_pos.transpose(0, 1) + + return normed_output diff --git a/py/sam2/modeling/memory_encoder.py b/py/sam2/modeling/memory_encoder.py new file mode 100644 index 0000000..1fbf1c8 --- /dev/null +++ b/py/sam2/modeling/memory_encoder.py @@ -0,0 +1,181 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...sam2.modeling.sam2_utils import DropPath, get_clones, LayerNorm2d + + +class MaskDownSampler(nn.Module): + """ + Progressively downsample a mask by total_stride, each time by stride. + Note that LayerNorm is applied per *token*, like in ViT. + + With each downsample (by a factor stride**2), channel capacity increases by the same factor. + In the end, we linearly project to embed_dim channels. + """ + + def __init__( + self, + embed_dim=256, + kernel_size=4, + stride=4, + padding=0, + total_stride=16, + activation=nn.GELU, + ): + super().__init__() + num_layers = int(math.log2(total_stride) // math.log2(stride)) + assert stride**num_layers == total_stride + self.encoder = nn.Sequential() + mask_in_chans, mask_out_chans = 1, 1 + for _ in range(num_layers): + mask_out_chans = mask_in_chans * (stride**2) + self.encoder.append( + nn.Conv2d( + mask_in_chans, + mask_out_chans, + kernel_size=kernel_size, + stride=stride, + padding=padding, + ) + ) + self.encoder.append(LayerNorm2d(mask_out_chans)) + self.encoder.append(activation()) + mask_in_chans = mask_out_chans + + self.encoder.append(nn.Conv2d(mask_out_chans, embed_dim, kernel_size=1)) + + def forward(self, x): + return self.encoder(x) + + +# Lightly adapted from ConvNext (https://github.com/facebookresearch/ConvNeXt) +class CXBlock(nn.Module): + r"""ConvNeXt Block. There are two equivalent implementations: + (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) + (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back + We use (2) as we find it slightly faster in PyTorch + + Args: + dim (int): Number of input channels. + drop_path (float): Stochastic depth rate. Default: 0.0 + layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. + """ + + def __init__( + self, + dim, + kernel_size=7, + padding=3, + drop_path=0.0, + layer_scale_init_value=1e-6, + use_dwconv=True, + ): + super().__init__() + self.dwconv = nn.Conv2d( + dim, + dim, + kernel_size=kernel_size, + padding=padding, + groups=dim if use_dwconv else 1, + ) # depthwise conv + self.norm = LayerNorm2d(dim, eps=1e-6) + self.pwconv1 = nn.Linear( + dim, 4 * dim + ) # pointwise/1x1 convs, implemented with linear layers + self.act = nn.GELU() + self.pwconv2 = nn.Linear(4 * dim, dim) + self.gamma = ( + nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) + if layer_scale_init_value > 0 + else None + ) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + def forward(self, x): + input = x + x = self.dwconv(x) + x = self.norm(x) + x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C) + x = self.pwconv1(x) + x = self.act(x) + x = self.pwconv2(x) + if self.gamma is not None: + x = self.gamma * x + x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W) + + x = input + self.drop_path(x) + return x + + +class Fuser(nn.Module): + def __init__(self, layer, num_layers, dim=None, input_projection=False): + super().__init__() + self.proj = nn.Identity() + self.layers = get_clones(layer, num_layers) + + if input_projection: + assert dim is not None + self.proj = nn.Conv2d(dim, dim, kernel_size=1) + + def forward(self, x): + # normally x: (N, C, H, W) + x = self.proj(x) + for layer in self.layers: + x = layer(x) + return x + + +class MemoryEncoder(nn.Module): + def __init__( + self, + out_dim, + mask_downsampler, + fuser, + position_encoding, + in_dim=256, # in_dim of pix_feats + ): + super().__init__() + + self.mask_downsampler = mask_downsampler + + self.pix_feat_proj = nn.Conv2d(in_dim, in_dim, kernel_size=1) + self.fuser = fuser + self.position_encoding = position_encoding + self.out_proj = nn.Identity() + if out_dim != in_dim: + self.out_proj = nn.Conv2d(in_dim, out_dim, kernel_size=1) + + def forward( + self, + pix_feat: torch.Tensor, + masks: torch.Tensor, + skip_mask_sigmoid: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + ## Process masks + # sigmoid, so that less domain shift from gt masks which are bool + if not skip_mask_sigmoid: + masks = F.sigmoid(masks) + masks = self.mask_downsampler(masks) + + ## Fuse pix_feats and downsampled masks + # in case the visual features are on CPU, cast them to CUDA + pix_feat = pix_feat.to(masks.device) + + x = self.pix_feat_proj(pix_feat) + x = x + masks + x = self.fuser(x) + x = self.out_proj(x) + + pos = self.position_encoding(x).to(x.dtype) + + return {"vision_features": x, "vision_pos_enc": [pos]} diff --git a/py/sam2/modeling/position_encoding.py b/py/sam2/modeling/position_encoding.py new file mode 100644 index 0000000..f4b57ae --- /dev/null +++ b/py/sam2/modeling/position_encoding.py @@ -0,0 +1,216 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Any, Optional, Tuple + +import numpy as np + +import torch +from torch import nn + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__( + self, + num_pos_feats, + temperature: int = 10000, + normalize: bool = True, + scale: Optional[float] = None, + ): + super().__init__() + assert num_pos_feats % 2 == 0, "Expecting even model width" + self.num_pos_feats = num_pos_feats // 2 + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + self.cache = {} + + def _encode_xy(self, x, y): + # The positions are expected to be normalized + assert len(x) == len(y) and x.ndim == y.ndim == 1 + x_embed = x * self.scale + y_embed = y * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, None] / dim_t + pos_y = y_embed[:, None] / dim_t + pos_x = torch.stack( + (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2 + ).flatten(1) + pos_y = torch.stack( + (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2 + ).flatten(1) + return pos_x, pos_y + + @torch.no_grad() + def encode_boxes(self, x, y, w, h): + pos_x, pos_y = self._encode_xy(x, y) + pos = torch.cat((pos_y, pos_x, h[:, None], w[:, None]), dim=1) + return pos + + encode = encode_boxes # Backwards compatibility + + @torch.no_grad() + def encode_points(self, x, y, labels): + (bx, nx), (by, ny), (bl, nl) = x.shape, y.shape, labels.shape + assert bx == by and nx == ny and bx == bl and nx == nl + pos_x, pos_y = self._encode_xy(x.flatten(), y.flatten()) + pos_x, pos_y = pos_x.reshape(bx, nx, -1), pos_y.reshape(by, ny, -1) + pos = torch.cat((pos_y, pos_x, labels[:, :, None]), dim=2) + return pos + + @torch.no_grad() + def forward(self, x: torch.Tensor): + cache_key = (x.shape[-2], x.shape[-1]) + if cache_key in self.cache: + return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1) + y_embed = ( + torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device) + .view(1, -1, 1) + .repeat(x.shape[0], 1, x.shape[-1]) + ) + x_embed = ( + torch.arange(1, x.shape[-1] + 1, dtype=torch.float32, device=x.device) + .view(1, 1, -1) + .repeat(x.shape[0], x.shape[-2], 1) + ) + + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + self.cache[cache_key] = pos[0] + return pos + + +class PositionEmbeddingRandom(nn.Module): + """ + Positional encoding using random spatial frequencies. + """ + + def __init__(self, num_pos_feats: int = 64, scale: Optional[float] = None) -> None: + super().__init__() + if scale is None or scale <= 0.0: + scale = 1.0 + self.register_buffer( + "positional_encoding_gaussian_matrix", + scale * torch.randn((2, num_pos_feats)), + ) + + def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: + """Positionally encode points that are normalized to [0,1].""" + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coords = 2 * coords - 1 + coords = coords @ self.positional_encoding_gaussian_matrix + coords = 2 * np.pi * coords + # outputs d_1 x ... x d_n x C shape + return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) + + def forward(self, size: Tuple[int, int]) -> torch.Tensor: + """Generate positional encoding for a grid of the specified size.""" + h, w = size + device: Any = self.positional_encoding_gaussian_matrix.device + grid = torch.ones((h, w), device=device, dtype=torch.float32) + y_embed = grid.cumsum(dim=0) - 0.5 + x_embed = grid.cumsum(dim=1) - 0.5 + y_embed = y_embed / h + x_embed = x_embed / w + + pe = self._pe_encoding(torch.stack([x_embed, y_embed], dim=-1)) + return pe.permute(2, 0, 1) # C x H x W + + def forward_with_coords( + self, coords_input: torch.Tensor, image_size: Tuple[int, int] + ) -> torch.Tensor: + """Positionally encode points that are not normalized to [0,1].""" + coords = coords_input.clone() + coords[:, :, 0] = coords[:, :, 0] / image_size[1] + coords[:, :, 1] = coords[:, :, 1] / image_size[0] + return self._pe_encoding(coords.to(torch.float)) # B x N x C + + +# Rotary Positional Encoding, adapted from: +# 1. https://github.com/meta-llama/codellama/blob/main/llama/model.py +# 2. https://github.com/naver-ai/rope-vit +# 3. https://github.com/lucidrains/rotary-embedding-torch + + +def init_t_xy(end_x: int, end_y: int): + t = torch.arange(end_x * end_y, dtype=torch.float32) + t_x = (t % end_x).float() + t_y = torch.div(t, end_x, rounding_mode="floor").float() + return t_x, t_y + + +def compute_axial_cis(dim: int, end_x: int, end_y: int, theta: float = 10000.0): + freqs_x = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) + freqs_y = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) + + t_x, t_y = init_t_xy(end_x, end_y) + freqs_x = torch.outer(t_x, freqs_x) + freqs_y = torch.outer(t_y, freqs_y) + freqs_cis_x = torch.polar(torch.ones_like(freqs_x), freqs_x) + freqs_cis_y = torch.polar(torch.ones_like(freqs_y), freqs_y) + return torch.cat([freqs_cis_x, freqs_cis_y], dim=-1) + + +def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): + ndim = x.ndim + assert 0 <= 1 < ndim + assert freqs_cis.shape == (x.shape[-2], x.shape[-1]) + shape = [d if i >= ndim - 2 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + +def apply_rotary_enc( + xq: torch.Tensor, + xk: torch.Tensor, + freqs_cis: torch.Tensor, + repeat_freqs_k: bool = False, +): + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) + xk_ = ( + torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) + if xk.shape[-2] != 0 + else None + ) + freqs_cis = reshape_for_broadcast(freqs_cis, xq_) + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) + if xk_ is None: + # no keys to rotate, due to dropout + return xq_out.type_as(xq).to(xq.device), xk + # repeat freqs along seq_len dim to match k seq_len + if repeat_freqs_k: + r = xk_.shape[-2] // xq_.shape[-2] + freqs_cis = freqs_cis.repeat(*([1] * (freqs_cis.ndim - 2)), r, 1) + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) + return xq_out.type_as(xq).to(xq.device), xk_out.type_as(xk).to(xk.device) diff --git a/py/sam2/modeling/sam/__init__.py b/py/sam2/modeling/sam/__init__.py new file mode 100644 index 0000000..5277f46 --- /dev/null +++ b/py/sam2/modeling/sam/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/py/sam2/modeling/sam/mask_decoder.py b/py/sam2/modeling/sam/mask_decoder.py new file mode 100644 index 0000000..4238cdf --- /dev/null +++ b/py/sam2/modeling/sam/mask_decoder.py @@ -0,0 +1,295 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Optional, Tuple, Type + +import torch +from torch import nn + +from ....sam2.modeling.sam2_utils import LayerNorm2d, MLP + + +class MaskDecoder(nn.Module): + def __init__( + self, + *, + transformer_dim: int, + transformer: nn.Module, + num_multimask_outputs: int = 3, + activation: Type[nn.Module] = nn.GELU, + iou_head_depth: int = 3, + iou_head_hidden_dim: int = 256, + use_high_res_features: bool = False, + iou_prediction_use_sigmoid=False, + dynamic_multimask_via_stability=False, + dynamic_multimask_stability_delta=0.05, + dynamic_multimask_stability_thresh=0.98, + pred_obj_scores: bool = False, + pred_obj_scores_mlp: bool = False, + use_multimask_token_for_obj_ptr: bool = False, + ) -> None: + """ + Predicts masks given an image and prompt embeddings, using a + transformer architecture. + + Arguments: + transformer_dim (int): the channel dimension of the transformer + transformer (nn.Module): the transformer used to predict masks + num_multimask_outputs (int): the number of masks to predict + when disambiguating masks + activation (nn.Module): the type of activation to use when + upscaling masks + iou_head_depth (int): the depth of the MLP used to predict + mask quality + iou_head_hidden_dim (int): the hidden dimension of the MLP + used to predict mask quality + """ + super().__init__() + self.transformer_dim = transformer_dim + self.transformer = transformer + + self.num_multimask_outputs = num_multimask_outputs + + self.iou_token = nn.Embedding(1, transformer_dim) + self.num_mask_tokens = num_multimask_outputs + 1 + self.mask_tokens = nn.Embedding(self.num_mask_tokens, transformer_dim) + + self.pred_obj_scores = pred_obj_scores + if self.pred_obj_scores: + self.obj_score_token = nn.Embedding(1, transformer_dim) + self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr + + self.output_upscaling = nn.Sequential( + nn.ConvTranspose2d( + transformer_dim, transformer_dim // 4, kernel_size=2, stride=2 + ), + LayerNorm2d(transformer_dim // 4), + activation(), + nn.ConvTranspose2d( + transformer_dim // 4, transformer_dim // 8, kernel_size=2, stride=2 + ), + activation(), + ) + self.use_high_res_features = use_high_res_features + if use_high_res_features: + self.conv_s0 = nn.Conv2d( + transformer_dim, transformer_dim // 8, kernel_size=1, stride=1 + ) + self.conv_s1 = nn.Conv2d( + transformer_dim, transformer_dim // 4, kernel_size=1, stride=1 + ) + + self.output_hypernetworks_mlps = nn.ModuleList( + [ + MLP(transformer_dim, transformer_dim, transformer_dim // 8, 3) + for i in range(self.num_mask_tokens) + ] + ) + + self.iou_prediction_head = MLP( + transformer_dim, + iou_head_hidden_dim, + self.num_mask_tokens, + iou_head_depth, + sigmoid_output=iou_prediction_use_sigmoid, + ) + if self.pred_obj_scores: + self.pred_obj_score_head = nn.Linear(transformer_dim, 1) + if pred_obj_scores_mlp: + self.pred_obj_score_head = MLP(transformer_dim, transformer_dim, 1, 3) + + # When outputting a single mask, optionally we can dynamically fall back to the best + # multimask output token if the single mask output token gives low stability scores. + self.dynamic_multimask_via_stability = dynamic_multimask_via_stability + self.dynamic_multimask_stability_delta = dynamic_multimask_stability_delta + self.dynamic_multimask_stability_thresh = dynamic_multimask_stability_thresh + + def forward( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + multimask_output: bool, + repeat_image: bool, + high_res_features: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Arguments: + image_embeddings (torch.Tensor): the embeddings from the image encoder + image_pe (torch.Tensor): positional encoding with the shape of image_embeddings + sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes + dense_prompt_embeddings (torch.Tensor): the embeddings of the mask inputs + multimask_output (bool): Whether to return multiple masks or a single + mask. + + Returns: + torch.Tensor: batched predicted masks + torch.Tensor: batched predictions of mask quality + torch.Tensor: batched SAM token for mask output + """ + masks, iou_pred, mask_tokens_out, object_score_logits = self.predict_masks( + image_embeddings=image_embeddings, + image_pe=image_pe, + sparse_prompt_embeddings=sparse_prompt_embeddings, + dense_prompt_embeddings=dense_prompt_embeddings, + repeat_image=repeat_image, + high_res_features=high_res_features, + ) + + # Select the correct mask or masks for output + if multimask_output: + masks = masks[:, 1:, :, :] + iou_pred = iou_pred[:, 1:] + elif self.dynamic_multimask_via_stability and not self.training: + masks, iou_pred = self._dynamic_multimask_via_stability(masks, iou_pred) + else: + masks = masks[:, 0:1, :, :] + iou_pred = iou_pred[:, 0:1] + + if multimask_output and self.use_multimask_token_for_obj_ptr: + sam_tokens_out = mask_tokens_out[:, 1:] # [b, 3, c] shape + else: + # Take the mask output token. Here we *always* use the token for single mask output. + # At test time, even if we track after 1-click (and using multimask_output=True), + # we still take the single mask token here. The rationale is that we always track + # after multiple clicks during training, so the past tokens seen during training + # are always the single mask token (and we'll let it be the object-memory token). + sam_tokens_out = mask_tokens_out[:, 0:1] # [b, 1, c] shape + + # Prepare output + return masks, iou_pred, sam_tokens_out, object_score_logits + + def predict_masks( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + dense_prompt_embeddings: torch.Tensor, + repeat_image: bool, + high_res_features: Optional[List[torch.Tensor]] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Predicts masks. See 'forward' for more details.""" + # Concatenate output tokens + s = 0 + if self.pred_obj_scores: + output_tokens = torch.cat( + [ + self.obj_score_token.weight, + self.iou_token.weight, + self.mask_tokens.weight, + ], + dim=0, + ) + s = 1 + else: + output_tokens = torch.cat( + [self.iou_token.weight, self.mask_tokens.weight], dim=0 + ) + output_tokens = output_tokens.unsqueeze(0).expand( + sparse_prompt_embeddings.size(0), -1, -1 + ) + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) + + # Expand per-image data in batch direction to be per-mask + if repeat_image: + src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0) + else: + assert image_embeddings.shape[0] == tokens.shape[0] + src = image_embeddings + src = src + dense_prompt_embeddings + assert ( + image_pe.size(0) == 1 + ), "image_pe should have size 1 in batch dim (from `get_dense_pe()`)" + pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0) + b, c, h, w = src.shape + + # Run the transformer + hs, src = self.transformer(src, pos_src, tokens) + iou_token_out = hs[:, s, :] + mask_tokens_out = hs[:, s + 1 : (s + 1 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + src = src.transpose(1, 2).view(b, c, h, w) + if not self.use_high_res_features: + upscaled_embedding = self.output_upscaling(src) + else: + dc1, ln1, act1, dc2, act2 = self.output_upscaling + feat_s0, feat_s1 = high_res_features + upscaled_embedding = act1(ln1(dc1(src) + feat_s1)) + upscaled_embedding = act2(dc2(upscaled_embedding) + feat_s0) + + hyper_in_list: List[torch.Tensor] = [] + for i in range(self.num_mask_tokens): + hyper_in_list.append( + self.output_hypernetworks_mlps[i](mask_tokens_out[:, i, :]) + ) + hyper_in = torch.stack(hyper_in_list, dim=1) + b, c, h, w = upscaled_embedding.shape + masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) + + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + if self.pred_obj_scores: + assert s == 1 + object_score_logits = self.pred_obj_score_head(hs[:, 0, :]) + else: + # Obj scores logits - default to 10.0, i.e. assuming the object is present, sigmoid(10)=1 + object_score_logits = 10.0 * iou_pred.new_ones(iou_pred.shape[0], 1) + + return masks, iou_pred, mask_tokens_out, object_score_logits + + def _get_stability_scores(self, mask_logits): + """ + Compute stability scores of the mask logits based on the IoU between upper and + lower thresholds, similar to https://github.com/fairinternal/onevision/pull/568. + """ + mask_logits = mask_logits.flatten(-2) + stability_delta = self.dynamic_multimask_stability_delta + area_i = torch.sum(mask_logits > stability_delta, dim=-1).float() + area_u = torch.sum(mask_logits > -stability_delta, dim=-1).float() + stability_scores = torch.where(area_u > 0, area_i / area_u, 1.0) + return stability_scores + + def _dynamic_multimask_via_stability(self, all_mask_logits, all_iou_scores): + """ + When outputting a single mask, if the stability score from the current single-mask + output (based on output token 0) falls below a threshold, we instead select from + multi-mask outputs (based on output token 1~3) the mask with the highest predicted + IoU score. This is intended to ensure a valid mask for both clicking and tracking. + """ + # The best mask from multimask output tokens (1~3) + multimask_logits = all_mask_logits[:, 1:, :, :] + multimask_iou_scores = all_iou_scores[:, 1:] + best_scores_inds = torch.argmax(multimask_iou_scores, dim=-1) + batch_inds = torch.arange( + multimask_iou_scores.size(0), device=all_iou_scores.device + ) + best_multimask_logits = multimask_logits[batch_inds, best_scores_inds] + best_multimask_logits = best_multimask_logits.unsqueeze(1) + best_multimask_iou_scores = multimask_iou_scores[batch_inds, best_scores_inds] + best_multimask_iou_scores = best_multimask_iou_scores.unsqueeze(1) + + # The mask from singlemask output token 0 and its stability score + singlemask_logits = all_mask_logits[:, 0:1, :, :] + singlemask_iou_scores = all_iou_scores[:, 0:1] + stability_scores = self._get_stability_scores(singlemask_logits) + is_stable = stability_scores >= self.dynamic_multimask_stability_thresh + + # Dynamically fall back to best multimask output upon low stability scores. + mask_logits_out = torch.where( + is_stable[..., None, None].expand_as(singlemask_logits), + singlemask_logits, + best_multimask_logits, + ) + iou_scores_out = torch.where( + is_stable.expand_as(singlemask_iou_scores), + singlemask_iou_scores, + best_multimask_iou_scores, + ) + return mask_logits_out, iou_scores_out diff --git a/py/sam2/modeling/sam/prompt_encoder.py b/py/sam2/modeling/sam/prompt_encoder.py new file mode 100644 index 0000000..fe125c7 --- /dev/null +++ b/py/sam2/modeling/sam/prompt_encoder.py @@ -0,0 +1,182 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Optional, Tuple, Type + +import torch +from torch import nn + +from ....sam2.modeling.position_encoding import PositionEmbeddingRandom + +from ....sam2.modeling.sam2_utils import LayerNorm2d + + +class PromptEncoder(nn.Module): + def __init__( + self, + embed_dim: int, + image_embedding_size: Tuple[int, int], + input_image_size: Tuple[int, int], + mask_in_chans: int, + activation: Type[nn.Module] = nn.GELU, + ) -> None: + """ + Encodes prompts for input to SAM's mask decoder. + + Arguments: + embed_dim (int): The prompts' embedding dimension + image_embedding_size (tuple(int, int)): The spatial size of the + image embedding, as (H, W). + input_image_size (int): The padded size of the image as input + to the image encoder, as (H, W). + mask_in_chans (int): The number of hidden channels used for + encoding input masks. + activation (nn.Module): The activation to use when encoding + input masks. + """ + super().__init__() + self.embed_dim = embed_dim + self.input_image_size = input_image_size + self.image_embedding_size = image_embedding_size + self.pe_layer = PositionEmbeddingRandom(embed_dim // 2) + + self.num_point_embeddings: int = 4 # pos/neg point + 2 box corners + point_embeddings = [ + nn.Embedding(1, embed_dim) for i in range(self.num_point_embeddings) + ] + self.point_embeddings = nn.ModuleList(point_embeddings) + self.not_a_point_embed = nn.Embedding(1, embed_dim) + + self.mask_input_size = ( + 4 * image_embedding_size[0], + 4 * image_embedding_size[1], + ) + self.mask_downscaling = nn.Sequential( + nn.Conv2d(1, mask_in_chans // 4, kernel_size=2, stride=2), + LayerNorm2d(mask_in_chans // 4), + activation(), + nn.Conv2d(mask_in_chans // 4, mask_in_chans, kernel_size=2, stride=2), + LayerNorm2d(mask_in_chans), + activation(), + nn.Conv2d(mask_in_chans, embed_dim, kernel_size=1), + ) + self.no_mask_embed = nn.Embedding(1, embed_dim) + + def get_dense_pe(self) -> torch.Tensor: + """ + Returns the positional encoding used to encode point prompts, + applied to a dense set of points the shape of the image encoding. + + Returns: + torch.Tensor: Positional encoding with shape + 1x(embed_dim)x(embedding_h)x(embedding_w) + """ + return self.pe_layer(self.image_embedding_size).unsqueeze(0) + + def _embed_points( + self, + points: torch.Tensor, + labels: torch.Tensor, + pad: bool, + ) -> torch.Tensor: + """Embeds point prompts.""" + points = points + 0.5 # Shift to center of pixel + if pad: + padding_point = torch.zeros((points.shape[0], 1, 2), device=points.device) + padding_label = -torch.ones((labels.shape[0], 1), device=labels.device) + points = torch.cat([points, padding_point], dim=1) + labels = torch.cat([labels, padding_label], dim=1) + point_embedding = self.pe_layer.forward_with_coords( + points, self.input_image_size + ) + point_embedding[labels == -1] = 0.0 + point_embedding[labels == -1] += self.not_a_point_embed.weight + point_embedding[labels == 0] += self.point_embeddings[0].weight + point_embedding[labels == 1] += self.point_embeddings[1].weight + point_embedding[labels == 2] += self.point_embeddings[2].weight + point_embedding[labels == 3] += self.point_embeddings[3].weight + return point_embedding + + def _embed_boxes(self, boxes: torch.Tensor) -> torch.Tensor: + """Embeds box prompts.""" + boxes = boxes + 0.5 # Shift to center of pixel + coords = boxes.reshape(-1, 2, 2) + corner_embedding = self.pe_layer.forward_with_coords( + coords, self.input_image_size + ) + corner_embedding[:, 0, :] += self.point_embeddings[2].weight + corner_embedding[:, 1, :] += self.point_embeddings[3].weight + return corner_embedding + + def _embed_masks(self, masks: torch.Tensor) -> torch.Tensor: + """Embeds mask inputs.""" + mask_embedding = self.mask_downscaling(masks) + return mask_embedding + + def _get_batch_size( + self, + points: Optional[Tuple[torch.Tensor, torch.Tensor]], + boxes: Optional[torch.Tensor], + masks: Optional[torch.Tensor], + ) -> int: + """ + Gets the batch size of the output given the batch size of the input prompts. + """ + if points is not None: + return points[0].shape[0] + elif boxes is not None: + return boxes.shape[0] + elif masks is not None: + return masks.shape[0] + else: + return 1 + + def _get_device(self) -> torch.device: + return self.point_embeddings[0].weight.device + + def forward( + self, + points: Optional[Tuple[torch.Tensor, torch.Tensor]], + boxes: Optional[torch.Tensor], + masks: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Embeds different types of prompts, returning both sparse and dense + embeddings. + + Arguments: + points (tuple(torch.Tensor, torch.Tensor) or none): point coordinates + and labels to embed. + boxes (torch.Tensor or none): boxes to embed + masks (torch.Tensor or none): masks to embed + + Returns: + torch.Tensor: sparse embeddings for the points and boxes, with shape + BxNx(embed_dim), where N is determined by the number of input points + and boxes. + torch.Tensor: dense embeddings for the masks, in the shape + Bx(embed_dim)x(embed_H)x(embed_W) + """ + bs = self._get_batch_size(points, boxes, masks) + sparse_embeddings = torch.empty( + (bs, 0, self.embed_dim), device=self._get_device() + ) + if points is not None: + coords, labels = points + point_embeddings = self._embed_points(coords, labels, pad=(boxes is None)) + sparse_embeddings = torch.cat([sparse_embeddings, point_embeddings], dim=1) + if boxes is not None: + box_embeddings = self._embed_boxes(boxes) + sparse_embeddings = torch.cat([sparse_embeddings, box_embeddings], dim=1) + + if masks is not None: + dense_embeddings = self._embed_masks(masks) + else: + dense_embeddings = self.no_mask_embed.weight.reshape(1, -1, 1, 1).expand( + bs, -1, self.image_embedding_size[0], self.image_embedding_size[1] + ) + + return sparse_embeddings, dense_embeddings diff --git a/py/sam2/modeling/sam/transformer.py b/py/sam2/modeling/sam/transformer.py new file mode 100644 index 0000000..fb71606 --- /dev/null +++ b/py/sam2/modeling/sam/transformer.py @@ -0,0 +1,347 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math +import warnings +from functools import partial +from typing import Tuple, Type + +import torch +import torch.nn.functional as F +from torch import nn, Tensor + +from ....sam2.modeling.position_encoding import apply_rotary_enc, compute_axial_cis +from ....sam2.modeling.sam2_utils import MLP + +from ....sam2.utils.misc import get_sdpa_settings +OLD_GPU, USE_FLASH_ATTN, MATH_KERNEL_ON = get_sdpa_settings() + +try: + from torch.nn.attention import SDPBackend, sdpa_kernel + backends = [] + if USE_FLASH_ATTN: + backends.append(SDPBackend.FLASH_ATTENTION) + if MATH_KERNEL_ON: + backends.append(SDPBackend.MATH) + if OLD_GPU: + backends.append(SDPBackend.EFFICIENT_ATTENTION) + OLD_TORCH = False +except: + OLD_TORCH = True + +warnings.simplefilter(action="ignore", category=FutureWarning) + +class TwoWayTransformer(nn.Module): + def __init__( + self, + depth: int, + embedding_dim: int, + num_heads: int, + mlp_dim: int, + activation: Type[nn.Module] = nn.ReLU, + attention_downsample_rate: int = 2, + ) -> None: + """ + A transformer decoder that attends to an input image using + queries whose positional embedding is supplied. + + Args: + depth (int): number of layers in the transformer + embedding_dim (int): the channel dimension for the input embeddings + num_heads (int): the number of heads for multihead attention. Must + divide embedding_dim + mlp_dim (int): the channel dimension internal to the MLP block + activation (nn.Module): the activation to use in the MLP block + """ + super().__init__() + self.depth = depth + self.embedding_dim = embedding_dim + self.num_heads = num_heads + self.mlp_dim = mlp_dim + self.layers = nn.ModuleList() + + for i in range(depth): + self.layers.append( + TwoWayAttentionBlock( + embedding_dim=embedding_dim, + num_heads=num_heads, + mlp_dim=mlp_dim, + activation=activation, + attention_downsample_rate=attention_downsample_rate, + skip_first_layer_pe=(i == 0), + ) + ) + + self.final_attn_token_to_image = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + self.norm_final_attn = nn.LayerNorm(embedding_dim) + + def forward( + self, + image_embedding: Tensor, + image_pe: Tensor, + point_embedding: Tensor, + ) -> Tuple[Tensor, Tensor]: + """ + Args: + image_embedding (torch.Tensor): image to attend to. Should be shape + B x embedding_dim x h x w for any h and w. + image_pe (torch.Tensor): the positional encoding to add to the image. Must + have the same shape as image_embedding. + point_embedding (torch.Tensor): the embedding to add to the query points. + Must have shape B x N_points x embedding_dim for any N_points. + + Returns: + torch.Tensor: the processed point_embedding + torch.Tensor: the processed image_embedding + """ + # BxCxHxW -> BxHWxC == B x N_image_tokens x C + bs, c, h, w = image_embedding.shape + image_embedding = image_embedding.flatten(2).permute(0, 2, 1) + image_pe = image_pe.flatten(2).permute(0, 2, 1) + + # Prepare queries + queries = point_embedding + keys = image_embedding + + # Apply transformer blocks and final layernorm + for layer in self.layers: + queries, keys = layer( + queries=queries, + keys=keys, + query_pe=point_embedding, + key_pe=image_pe, + ) + + # Apply the final attention layer from the points to the image + q = queries + point_embedding + k = keys + image_pe + attn_out = self.final_attn_token_to_image(q=q, k=k, v=keys) + queries = queries + attn_out + queries = self.norm_final_attn(queries) + + return queries, keys + + +class TwoWayAttentionBlock(nn.Module): + def __init__( + self, + embedding_dim: int, + num_heads: int, + mlp_dim: int = 2048, + activation: Type[nn.Module] = nn.ReLU, + attention_downsample_rate: int = 2, + skip_first_layer_pe: bool = False, + ) -> None: + """ + A transformer block with four layers: (1) self-attention of sparse + inputs, (2) cross attention of sparse inputs to dense inputs, (3) mlp + block on sparse inputs, and (4) cross attention of dense inputs to sparse + inputs. + + Arguments: + embedding_dim (int): the channel dimension of the embeddings + num_heads (int): the number of heads in the attention layers + mlp_dim (int): the hidden dimension of the mlp block + activation (nn.Module): the activation of the mlp block + skip_first_layer_pe (bool): skip the PE on the first layer + """ + super().__init__() + self.self_attn = Attention(embedding_dim, num_heads) + self.norm1 = nn.LayerNorm(embedding_dim) + + self.cross_attn_token_to_image = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + self.norm2 = nn.LayerNorm(embedding_dim) + + self.mlp = MLP( + embedding_dim, mlp_dim, embedding_dim, num_layers=2, activation=activation + ) + self.norm3 = nn.LayerNorm(embedding_dim) + + self.norm4 = nn.LayerNorm(embedding_dim) + self.cross_attn_image_to_token = Attention( + embedding_dim, num_heads, downsample_rate=attention_downsample_rate + ) + + self.skip_first_layer_pe = skip_first_layer_pe + + def forward( + self, queries: Tensor, keys: Tensor, query_pe: Tensor, key_pe: Tensor + ) -> Tuple[Tensor, Tensor]: + # Self attention block + if self.skip_first_layer_pe: + queries = self.self_attn(q=queries, k=queries, v=queries) + else: + q = queries + query_pe + attn_out = self.self_attn(q=q, k=q, v=queries) + queries = queries + attn_out + queries = self.norm1(queries) + + # Cross attention block, tokens attending to image embedding + q = queries + query_pe + k = keys + key_pe + attn_out = self.cross_attn_token_to_image(q=q, k=k, v=keys) + queries = queries + attn_out + queries = self.norm2(queries) + + # MLP block + mlp_out = self.mlp(queries) + queries = queries + mlp_out + queries = self.norm3(queries) + + # Cross attention block, image embedding attending to tokens + q = queries + query_pe + k = keys + key_pe + attn_out = self.cross_attn_image_to_token(q=k, k=q, v=queries) + keys = keys + attn_out + keys = self.norm4(keys) + + return queries, keys + + +class Attention(nn.Module): + """ + An attention layer that allows for downscaling the size of the embedding + after projection to queries, keys, and values. + """ + + def __init__( + self, + embedding_dim: int, + num_heads: int, + downsample_rate: int = 1, + dropout: float = 0.0, + kv_in_dim: int = None, + ) -> None: + super().__init__() + self.embedding_dim = embedding_dim + self.kv_in_dim = kv_in_dim if kv_in_dim is not None else embedding_dim + self.internal_dim = embedding_dim // downsample_rate + self.num_heads = num_heads + assert ( + self.internal_dim % num_heads == 0 + ), "num_heads must divide embedding_dim." + + self.q_proj = nn.Linear(embedding_dim, self.internal_dim) + self.k_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.v_proj = nn.Linear(self.kv_in_dim, self.internal_dim) + self.out_proj = nn.Linear(self.internal_dim, embedding_dim) + + self.dropout_p = dropout + + def _separate_heads(self, x: Tensor, num_heads: int) -> Tensor: + b, n, c = x.shape + x = x.reshape(b, n, num_heads, c // num_heads) + return x.transpose(1, 2) # B x N_heads x N_tokens x C_per_head + + def _recombine_heads(self, x: Tensor) -> Tensor: + b, n_heads, n_tokens, c_per_head = x.shape + x = x.transpose(1, 2) + return x.reshape(b, n_tokens, n_heads * c_per_head) # B x N_tokens x C + + def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tensor: + # Input projections + q = self.q_proj(q) + k = self.k_proj(k) + v = self.v_proj(v) + + # Separate into heads + q = self._separate_heads(q, self.num_heads) + k = self._separate_heads(k, self.num_heads) + v = self._separate_heads(v, self.num_heads) + + dropout_p = self.dropout_p if self.training else 0.0 + # Attention + if not OLD_TORCH: + if not MATH_KERNEL_ON and OLD_GPU and dropout_p > 0.0: + backends.append(SDPBackend.MATH) + with sdpa_kernel(backends): + out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p) + else: + with torch.backends.cuda.sdp_kernel( + enable_flash=USE_FLASH_ATTN, + enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON, + enable_mem_efficient=OLD_GPU, + ): + out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p) + out = self._recombine_heads(out) + out = self.out_proj(out) + + return out + + +class RoPEAttention(Attention): + """Attention with rotary position encoding.""" + + def __init__( + self, + *args, + rope_theta=10000.0, + # whether to repeat q rope to match k length + # this is needed for cross-attention to memories + rope_k_repeat=False, + feat_sizes=(32, 32), # [w, h] for stride 16 feats at 512 resolution + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.compute_cis = partial( + compute_axial_cis, dim=self.internal_dim // self.num_heads, theta=rope_theta + ) + freqs_cis = self.compute_cis(end_x=feat_sizes[0], end_y=feat_sizes[1]) + self.freqs_cis = freqs_cis + self.rope_k_repeat = rope_k_repeat + + def forward( + self, q: Tensor, k: Tensor, v: Tensor, num_k_exclude_rope: int = 0 + ) -> Tensor: + # Input projections + q = self.q_proj(q) + k = self.k_proj(k) + v = self.v_proj(v) + + # Separate into heads + q = self._separate_heads(q, self.num_heads) + k = self._separate_heads(k, self.num_heads) + v = self._separate_heads(v, self.num_heads) + + # Apply rotary position encoding + w = h = math.sqrt(q.shape[-2]) + self.freqs_cis = self.freqs_cis.to(q.device) + if self.freqs_cis.shape[0] != q.shape[-2]: + self.freqs_cis = self.compute_cis(end_x=w, end_y=h).to(q.device) + if q.shape[-2] != k.shape[-2]: + assert self.rope_k_repeat + + num_k_rope = k.size(-2) - num_k_exclude_rope + q, k[:, :, :num_k_rope] = apply_rotary_enc( + q, + k[:, :, :num_k_rope], + freqs_cis=self.freqs_cis, + repeat_freqs_k=self.rope_k_repeat, + ) + + dropout_p = self.dropout_p if self.training else 0.0 + # Attention + if not OLD_TORCH: + if not MATH_KERNEL_ON and OLD_GPU and dropout_p > 0.0: + backends.append(SDPBackend.MATH) + with sdpa_kernel(backends): + out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p) + else: + with torch.backends.cuda.sdp_kernel( + enable_flash=USE_FLASH_ATTN, + enable_math=(OLD_GPU and dropout_p > 0.0) or MATH_KERNEL_ON, + enable_mem_efficient=OLD_GPU, + ): + out = F.scaled_dot_product_attention(q, k, v, dropout_p=dropout_p) + out = self._recombine_heads(out) + out = self.out_proj(out) + + return out diff --git a/py/sam2/modeling/sam2_base.py b/py/sam2/modeling/sam2_base.py new file mode 100644 index 0000000..a18a0a7 --- /dev/null +++ b/py/sam2/modeling/sam2_base.py @@ -0,0 +1,829 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.distributed +import torch.nn.functional as F + +from torch.nn.init import trunc_normal_ + +from ...sam2.modeling.sam.mask_decoder import MaskDecoder +from ...sam2.modeling.sam.prompt_encoder import PromptEncoder +from ...sam2.modeling.sam.transformer import TwoWayTransformer +from ...sam2.modeling.sam2_utils import get_1d_sine_pe, MLP, select_closest_cond_frames + +# a large negative value as a placeholder score for missing objects +NO_OBJ_SCORE = -1024.0 + + +class SAM2Base(torch.nn.Module): + def __init__( + self, + image_encoder, + memory_attention, + memory_encoder, + num_maskmem=7, # default 1 input frame + 6 previous frames + image_size=512, + backbone_stride=16, # stride of the image backbone output + sigmoid_scale_for_mem_enc=1.0, # scale factor for mask sigmoid prob + sigmoid_bias_for_mem_enc=0.0, # bias factor for mask sigmoid prob + # During evaluation, whether to binarize the sigmoid mask logits on interacted frames with clicks + binarize_mask_from_pts_for_mem_enc=False, + use_mask_input_as_output_without_sam=False, # on frames with mask input, whether to directly output the input mask without using a SAM prompt encoder + mask decoder + # The maximum number of conditioning frames to participate in the memory attention (-1 means no limit; if there are more conditioning frames than this limit, + # we only cross-attend to the temporally closest `max_cond_frames_in_attn` conditioning frames in the encoder when tracking each frame). This gives the model + # a temporal locality when handling a large number of annotated frames (since closer frames should be more important) and also avoids GPU OOM. + max_cond_frames_in_attn=-1, + # on the first frame, whether to directly add the no-memory embedding to the image feature + # (instead of using the transformer encoder) + directly_add_no_mem_embed=False, + # whether to use high-resolution feature maps in the SAM mask decoder + use_high_res_features_in_sam=False, + # whether to output multiple (3) masks for the first click on initial conditioning frames + multimask_output_in_sam=False, + # the minimum and maximum number of clicks to use multimask_output_in_sam (only relevant when `multimask_output_in_sam=True`; + # default is 1 for both, meaning that only the first click gives multimask output; also note that a box counts as two points) + multimask_min_pt_num=1, + multimask_max_pt_num=1, + # whether to also use multimask output for tracking (not just for the first click on initial conditioning frames; only relevant when `multimask_output_in_sam=True`) + multimask_output_for_tracking=False, + # Whether to use multimask tokens for obj ptr; Only relevant when both + # use_obj_ptrs_in_encoder=True and multimask_output_for_tracking=True + use_multimask_token_for_obj_ptr: bool = False, + # whether to use sigmoid to restrict ious prediction to [0-1] + iou_prediction_use_sigmoid=False, + # The memory bank's temporal stride during evaluation (i.e. the `r` parameter in XMem and Cutie; XMem and Cutie use r=5). + # For r>1, the (self.num_maskmem - 1) non-conditioning memory frames consist of + # (self.num_maskmem - 2) nearest frames from every r-th frames, plus the last frame. + memory_temporal_stride_for_eval=1, + # if `add_all_frames_to_correct_as_cond` is True, we also append to the conditioning frame list any frame that receives a later correction click + # if `add_all_frames_to_correct_as_cond` is False, we conditioning frame list to only use those initial conditioning frames + add_all_frames_to_correct_as_cond=False, + # whether to apply non-overlapping constraints on the object masks in the memory encoder during evaluation (to avoid/alleviate superposing masks) + non_overlap_masks_for_mem_enc=False, + # whether to cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder=False, + # the maximum number of object pointers from other frames in encoder cross attention (only relevant when `use_obj_ptrs_in_encoder=True`) + max_obj_ptrs_in_encoder=16, + # whether to add temporal positional encoding to the object pointers in the encoder (only relevant when `use_obj_ptrs_in_encoder=True`) + add_tpos_enc_to_obj_ptrs=True, + # whether to add an extra linear projection layer for the temporal positional encoding in the object pointers to avoid potential interference + # with spatial positional encoding (only relevant when both `use_obj_ptrs_in_encoder=True` and `add_tpos_enc_to_obj_ptrs=True`) + proj_tpos_enc_in_obj_ptrs=False, + # whether to only attend to object pointers in the past (before the current frame) in the encoder during evaluation + # (only relevant when `use_obj_ptrs_in_encoder=True`; this might avoid pointer information too far in the future to distract the initial tracking) + only_obj_ptrs_in_the_past_for_eval=False, + # Whether to predict if there is an object in the frame + pred_obj_scores: bool = False, + # Whether to use an MLP to predict object scores + pred_obj_scores_mlp: bool = False, + # Only relevant if pred_obj_scores=True and use_obj_ptrs_in_encoder=True; + # Whether to have a fixed no obj pointer when there is no object present + # or to use it as an additive embedding with obj_ptr produced by decoder + fixed_no_obj_ptr: bool = False, + # Soft no object, i.e. mix in no_obj_ptr softly, + # hope to make recovery easier if there is a mistake and mitigate accumulation of errors + soft_no_obj_ptr: bool = False, + use_mlp_for_obj_ptr_proj: bool = False, + # extra arguments used to construct the SAM mask decoder; if not None, it should be a dict of kwargs to be passed into `MaskDecoder` class. + sam_mask_decoder_extra_args=None, + compile_image_encoder: bool = False, + ): + super().__init__() + + # Part 1: the image backbone + self.image_encoder = image_encoder + # Use level 0, 1, 2 for high-res setting, or just level 2 for the default setting + self.use_high_res_features_in_sam = use_high_res_features_in_sam + self.num_feature_levels = 3 if use_high_res_features_in_sam else 1 + self.use_obj_ptrs_in_encoder = use_obj_ptrs_in_encoder + self.max_obj_ptrs_in_encoder = max_obj_ptrs_in_encoder + if use_obj_ptrs_in_encoder: + # A conv layer to downsample the mask prompt to stride 4 (the same stride as + # low-res SAM mask logits) and to change its scales from 0~1 to SAM logit scale, + # so that it can be fed into the SAM mask decoder to generate a pointer. + self.mask_downsample = torch.nn.Conv2d(1, 1, kernel_size=4, stride=4) + self.add_tpos_enc_to_obj_ptrs = add_tpos_enc_to_obj_ptrs + if proj_tpos_enc_in_obj_ptrs: + assert add_tpos_enc_to_obj_ptrs # these options need to be used together + self.proj_tpos_enc_in_obj_ptrs = proj_tpos_enc_in_obj_ptrs + self.only_obj_ptrs_in_the_past_for_eval = only_obj_ptrs_in_the_past_for_eval + + # Part 2: memory attention to condition current frame's visual features + # with memories (and obj ptrs) from past frames + self.memory_attention = memory_attention + self.hidden_dim = memory_attention.d_model + + # Part 3: memory encoder for the previous frame's outputs + self.memory_encoder = memory_encoder + self.mem_dim = self.hidden_dim + if hasattr(self.memory_encoder, "out_proj") and hasattr( + self.memory_encoder.out_proj, "weight" + ): + # if there is compression of memories along channel dim + self.mem_dim = self.memory_encoder.out_proj.weight.shape[0] + self.num_maskmem = num_maskmem # Number of memories accessible + # Temporal encoding of the memories + self.maskmem_tpos_enc = torch.nn.Parameter( + torch.zeros(num_maskmem, 1, 1, self.mem_dim) + ) + trunc_normal_(self.maskmem_tpos_enc, std=0.02) + # a single token to indicate no memory embedding from previous frames + self.no_mem_embed = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + self.no_mem_pos_enc = torch.nn.Parameter(torch.zeros(1, 1, self.hidden_dim)) + trunc_normal_(self.no_mem_embed, std=0.02) + trunc_normal_(self.no_mem_pos_enc, std=0.02) + self.directly_add_no_mem_embed = directly_add_no_mem_embed + # Apply sigmoid to the output raw mask logits (to turn them from + # range (-inf, +inf) to range (0, 1)) before feeding them into the memory encoder + self.sigmoid_scale_for_mem_enc = sigmoid_scale_for_mem_enc + self.sigmoid_bias_for_mem_enc = sigmoid_bias_for_mem_enc + self.binarize_mask_from_pts_for_mem_enc = binarize_mask_from_pts_for_mem_enc + self.non_overlap_masks_for_mem_enc = non_overlap_masks_for_mem_enc + self.memory_temporal_stride_for_eval = memory_temporal_stride_for_eval + # On frames with mask input, whether to directly output the input mask without + # using a SAM prompt encoder + mask decoder + self.use_mask_input_as_output_without_sam = use_mask_input_as_output_without_sam + self.multimask_output_in_sam = multimask_output_in_sam + self.multimask_min_pt_num = multimask_min_pt_num + self.multimask_max_pt_num = multimask_max_pt_num + self.multimask_output_for_tracking = multimask_output_for_tracking + self.use_multimask_token_for_obj_ptr = use_multimask_token_for_obj_ptr + self.iou_prediction_use_sigmoid = iou_prediction_use_sigmoid + + # Part 4: SAM-style prompt encoder (for both mask and point inputs) + # and SAM-style mask decoder for the final mask output + self.image_size = image_size + self.backbone_stride = backbone_stride + self.sam_mask_decoder_extra_args = sam_mask_decoder_extra_args + self.pred_obj_scores = pred_obj_scores + self.pred_obj_scores_mlp = pred_obj_scores_mlp + self.fixed_no_obj_ptr = fixed_no_obj_ptr + self.soft_no_obj_ptr = soft_no_obj_ptr + if self.fixed_no_obj_ptr: + assert self.pred_obj_scores + assert self.use_obj_ptrs_in_encoder + if self.pred_obj_scores and self.use_obj_ptrs_in_encoder: + self.no_obj_ptr = torch.nn.Parameter(torch.zeros(1, self.hidden_dim)) + trunc_normal_(self.no_obj_ptr, std=0.02) + self.use_mlp_for_obj_ptr_proj = use_mlp_for_obj_ptr_proj + + self._build_sam_heads() + self.add_all_frames_to_correct_as_cond = add_all_frames_to_correct_as_cond + self.max_cond_frames_in_attn = max_cond_frames_in_attn + + # Model compilation + if compile_image_encoder: + # Compile the forward function (not the full module) to allow loading checkpoints. + print( + "Image encoder compilation is enabled. First forward pass will be slow." + ) + self.image_encoder.forward = torch.compile( + self.image_encoder.forward, + mode="max-autotune", + fullgraph=True, + dynamic=False, + ) + + @property + def device(self): + return next(self.parameters()).device + + def forward(self, *args, **kwargs): + raise NotImplementedError( + "Please use the corresponding methods in SAM2VideoPredictor for inference." + "See notebooks/video_predictor_example.ipynb for an example." + ) + + def _build_sam_heads(self): + """Build SAM-style prompt encoder and mask decoder.""" + self.sam_prompt_embed_dim = self.hidden_dim + self.sam_image_embedding_size = self.image_size // self.backbone_stride + + # build PromptEncoder and MaskDecoder from SAM + # (their hyperparameters like `mask_in_chans=16` are from SAM code) + self.sam_prompt_encoder = PromptEncoder( + embed_dim=self.sam_prompt_embed_dim, + image_embedding_size=( + self.sam_image_embedding_size, + self.sam_image_embedding_size, + ), + input_image_size=(self.image_size, self.image_size), + mask_in_chans=16, + ) + self.sam_mask_decoder = MaskDecoder( + num_multimask_outputs=3, + transformer=TwoWayTransformer( + depth=2, + embedding_dim=self.sam_prompt_embed_dim, + mlp_dim=2048, + num_heads=8, + ), + transformer_dim=self.sam_prompt_embed_dim, + iou_head_depth=3, + iou_head_hidden_dim=256, + use_high_res_features=self.use_high_res_features_in_sam, + iou_prediction_use_sigmoid=self.iou_prediction_use_sigmoid, + pred_obj_scores=self.pred_obj_scores, + pred_obj_scores_mlp=self.pred_obj_scores_mlp, + use_multimask_token_for_obj_ptr=self.use_multimask_token_for_obj_ptr, + **(self.sam_mask_decoder_extra_args or {}), + ) + if self.use_obj_ptrs_in_encoder: + # a linear projection on SAM output tokens to turn them into object pointers + self.obj_ptr_proj = torch.nn.Linear(self.hidden_dim, self.hidden_dim) + if self.use_mlp_for_obj_ptr_proj: + self.obj_ptr_proj = MLP( + self.hidden_dim, self.hidden_dim, self.hidden_dim, 3 + ) + else: + self.obj_ptr_proj = torch.nn.Identity() + if self.proj_tpos_enc_in_obj_ptrs: + # a linear projection on temporal positional encoding in object pointers to + # avoid potential interference with spatial positional encoding + self.obj_ptr_tpos_proj = torch.nn.Linear(self.hidden_dim, self.mem_dim) + else: + self.obj_ptr_tpos_proj = torch.nn.Identity() + + def _forward_sam_heads( + self, + backbone_features, + point_inputs=None, + mask_inputs=None, + high_res_features=None, + multimask_output=False, + ): + """ + Forward SAM prompt encoders and mask heads. + + Inputs: + - backbone_features: image features of [B, C, H, W] shape + - point_inputs: a dictionary with "point_coords" and "point_labels", where + 1) "point_coords" has [B, P, 2] shape and float32 dtype and contains the + absolute pixel-unit coordinate in (x, y) format of the P input points + 2) "point_labels" has shape [B, P] and int32 dtype, where 1 means + positive clicks, 0 means negative clicks, and -1 means padding + - mask_inputs: a mask of [B, 1, H*16, W*16] shape, float or bool, with the + same spatial size as the image. + - high_res_features: either 1) None or 2) or a list of length 2 containing + two feature maps of [B, C, 4*H, 4*W] and [B, C, 2*H, 2*W] shapes respectively, + which will be used as high-resolution feature maps for SAM decoder. + - multimask_output: if it's True, we output 3 candidate masks and their 3 + corresponding IoU estimates, and if it's False, we output only 1 mask and + its corresponding IoU estimate. + + Outputs: + - low_res_multimasks: [B, M, H*4, W*4] shape (where M = 3 if + `multimask_output=True` and M = 1 if `multimask_output=False`), the SAM + output mask logits (before sigmoid) for the low-resolution masks, with 4x + the resolution (1/4 stride) of the input backbone_features. + - high_res_multimasks: [B, M, H*16, W*16] shape (where M = 3 + if `multimask_output=True` and M = 1 if `multimask_output=False`), + upsampled from the low-resolution masks, with shape size as the image + (stride is 1 pixel). + - ious, [B, M] shape, where (where M = 3 if `multimask_output=True` and M = 1 + if `multimask_output=False`), the estimated IoU of each output mask. + - low_res_masks: [B, 1, H*4, W*4] shape, the best mask in `low_res_multimasks`. + If `multimask_output=True`, it's the mask with the highest IoU estimate. + If `multimask_output=False`, it's the same as `low_res_multimasks`. + - high_res_masks: [B, 1, H*16, W*16] shape, the best mask in `high_res_multimasks`. + If `multimask_output=True`, it's the mask with the highest IoU estimate. + If `multimask_output=False`, it's the same as `high_res_multimasks`. + - obj_ptr: [B, C] shape, the object pointer vector for the output mask, extracted + based on the output token from the SAM mask decoder. + """ + B = backbone_features.size(0) + device = backbone_features.device + assert backbone_features.size(1) == self.sam_prompt_embed_dim + assert backbone_features.size(2) == self.sam_image_embedding_size + assert backbone_features.size(3) == self.sam_image_embedding_size + + # a) Handle point prompts + if point_inputs is not None: + sam_point_coords = point_inputs["point_coords"] + sam_point_labels = point_inputs["point_labels"] + assert sam_point_coords.size(0) == B and sam_point_labels.size(0) == B + else: + # If no points are provide, pad with an empty point (with label -1) + sam_point_coords = torch.zeros(B, 1, 2, device=device) + sam_point_labels = -torch.ones(B, 1, dtype=torch.int32, device=device) + + # b) Handle mask prompts + if mask_inputs is not None: + # If mask_inputs is provided, downsize it into low-res mask input if needed + # and feed it as a dense mask prompt into the SAM mask encoder + assert len(mask_inputs.shape) == 4 and mask_inputs.shape[:2] == (B, 1) + if mask_inputs.shape[-2:] != self.sam_prompt_encoder.mask_input_size: + sam_mask_prompt = F.interpolate( + mask_inputs.float(), + size=self.sam_prompt_encoder.mask_input_size, + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ) + else: + sam_mask_prompt = mask_inputs + else: + # Otherwise, simply feed None (and SAM's prompt encoder will add + # a learned `no_mask_embed` to indicate no mask input in this case). + sam_mask_prompt = None + + sparse_embeddings, dense_embeddings = self.sam_prompt_encoder( + points=(sam_point_coords, sam_point_labels), + boxes=None, + masks=sam_mask_prompt, + ) + ( + low_res_multimasks, + ious, + sam_output_tokens, + object_score_logits, + ) = self.sam_mask_decoder( + image_embeddings=backbone_features, + image_pe=self.sam_prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + repeat_image=False, # the image is already batched + high_res_features=high_res_features, + ) + if self.pred_obj_scores: + is_obj_appearing = object_score_logits > 0 + + # Mask used for spatial memories is always a *hard* choice between obj and no obj, + # consistent with the actual mask prediction + low_res_multimasks = torch.where( + is_obj_appearing[:, None, None], + low_res_multimasks, + NO_OBJ_SCORE, + ) + + # convert masks from possibly bfloat16 (or float16) to float32 + # (older PyTorch versions before 2.1 don't support `interpolate` on bf16) + low_res_multimasks = low_res_multimasks.float() + high_res_multimasks = F.interpolate( + low_res_multimasks, + size=(self.image_size, self.image_size), + mode="bilinear", + align_corners=False, + ) + + sam_output_token = sam_output_tokens[:, 0] + if multimask_output: + # take the best mask prediction (with the highest IoU estimation) + best_iou_inds = torch.argmax(ious, dim=-1) + batch_inds = torch.arange(B, device=device) + low_res_masks = low_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) + high_res_masks = high_res_multimasks[batch_inds, best_iou_inds].unsqueeze(1) + if sam_output_tokens.size(1) > 1: + sam_output_token = sam_output_tokens[batch_inds, best_iou_inds] + else: + low_res_masks, high_res_masks = low_res_multimasks, high_res_multimasks + + # Extract object pointer from the SAM output token (with occlusion handling) + obj_ptr = self.obj_ptr_proj(sam_output_token) + if self.pred_obj_scores: + # Allow *soft* no obj ptr, unlike for masks + if self.soft_no_obj_ptr: + # Only hard possible with gt + assert not self.teacher_force_obj_scores_for_mem + lambda_is_obj_appearing = object_score_logits.sigmoid() + else: + lambda_is_obj_appearing = is_obj_appearing.float() + + if self.fixed_no_obj_ptr: + obj_ptr = lambda_is_obj_appearing * obj_ptr + obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr + + return ( + low_res_multimasks, + high_res_multimasks, + ious, + low_res_masks, + high_res_masks, + obj_ptr, + object_score_logits, + ) + + def _use_mask_as_output(self, backbone_features, high_res_features, mask_inputs): + """ + Directly turn binary `mask_inputs` into a output mask logits without using SAM. + (same input and output shapes as in _forward_sam_heads above). + """ + # Use -10/+10 as logits for neg/pos pixels (very close to 0/1 in prob after sigmoid). + out_scale, out_bias = 20.0, -10.0 # sigmoid(-10.0)=4.5398e-05 + mask_inputs_float = mask_inputs.float() + high_res_masks = mask_inputs_float * out_scale + out_bias + low_res_masks = F.interpolate( + high_res_masks, + size=(high_res_masks.size(-2) // 4, high_res_masks.size(-1) // 4), + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ) + # a dummy IoU prediction of all 1's under mask input + ious = mask_inputs.new_ones(mask_inputs.size(0), 1).float() + if not self.use_obj_ptrs_in_encoder: + # all zeros as a dummy object pointer (of shape [B, C]) + obj_ptr = torch.zeros( + mask_inputs.size(0), self.hidden_dim, device=mask_inputs.device + ) + else: + # produce an object pointer using the SAM decoder from the mask input + _, _, _, _, _, obj_ptr, _ = self._forward_sam_heads( + backbone_features=backbone_features, + mask_inputs=self.mask_downsample(mask_inputs_float), + high_res_features=high_res_features, + ) + # In this method, we are treating mask_input as output, e.g. using it directly to create spatial mem; + # Below, we follow the same design axiom to use mask_input to decide if obj appears or not instead of relying + # on the object_scores from the SAM decoder. + is_obj_appearing = torch.any(mask_inputs.flatten(1).float() > 0.0, dim=1) + is_obj_appearing = is_obj_appearing[..., None] + lambda_is_obj_appearing = is_obj_appearing.float() + object_score_logits = out_scale * lambda_is_obj_appearing + out_bias + if self.pred_obj_scores: + if self.fixed_no_obj_ptr: + obj_ptr = lambda_is_obj_appearing * obj_ptr + obj_ptr = obj_ptr + (1 - lambda_is_obj_appearing) * self.no_obj_ptr + + return ( + low_res_masks, + high_res_masks, + ious, + low_res_masks, + high_res_masks, + obj_ptr, + object_score_logits, + ) + + def forward_image(self, img_batch: torch.Tensor): + """Get the image feature on the input batch.""" + backbone_out = self.image_encoder(img_batch) + if self.use_high_res_features_in_sam: + # precompute projected level 0 and level 1 features in SAM decoder + # to avoid running it again on every SAM click + backbone_out["backbone_fpn"][0] = self.sam_mask_decoder.conv_s0( + backbone_out["backbone_fpn"][0] + ) + backbone_out["backbone_fpn"][1] = self.sam_mask_decoder.conv_s1( + backbone_out["backbone_fpn"][1] + ) + return backbone_out + + def _prepare_backbone_features(self, backbone_out): + """Prepare and flatten visual features.""" + backbone_out = backbone_out.copy() + assert len(backbone_out["backbone_fpn"]) == len(backbone_out["vision_pos_enc"]) + assert len(backbone_out["backbone_fpn"]) >= self.num_feature_levels + + feature_maps = backbone_out["backbone_fpn"][-self.num_feature_levels :] + vision_pos_embeds = backbone_out["vision_pos_enc"][-self.num_feature_levels :] + + feat_sizes = [(x.shape[-2], x.shape[-1]) for x in vision_pos_embeds] + # flatten NxCxHxW to HWxNxC + vision_feats = [x.flatten(2).permute(2, 0, 1) for x in feature_maps] + vision_pos_embeds = [x.flatten(2).permute(2, 0, 1) for x in vision_pos_embeds] + + return backbone_out, vision_feats, vision_pos_embeds, feat_sizes + + def _prepare_memory_conditioned_features( + self, + frame_idx, + is_init_cond_frame, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + output_dict, + num_frames, + track_in_reverse=False, # tracking in reverse time order (for demo usage) + ): + """Fuse the current frame's visual feature map with previous memory.""" + B = current_vision_feats[-1].size(1) # batch size on this frame + C = self.hidden_dim + H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size + device = current_vision_feats[-1].device + # The case of `self.num_maskmem == 0` below is primarily used for reproducing SAM on images. + # In this case, we skip the fusion with any memory. + if self.num_maskmem == 0: # Disable memory and skip fusion + pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W) + return pix_feat + + num_obj_ptr_tokens = 0 + # Step 1: condition the visual features of the current frame on previous memories + if not is_init_cond_frame: + # Retrieve the memories encoded with the maskmem backbone + to_cat_memory, to_cat_memory_pos_embed = [], [] + # Add conditioning frames's output first (all cond frames have t_pos=0 for + # when getting temporal positional embedding below) + assert len(output_dict["cond_frame_outputs"]) > 0 + # Select a maximum number of temporally closest cond frames for cross attention + cond_outputs = output_dict["cond_frame_outputs"] + selected_cond_outputs, unselected_cond_outputs = select_closest_cond_frames( + frame_idx, cond_outputs, self.max_cond_frames_in_attn + ) + t_pos_and_prevs = [(0, out) for out in selected_cond_outputs.values()] + # Add last (self.num_maskmem - 1) frames before current frame for non-conditioning memory + # the earliest one has t_pos=1 and the latest one has t_pos=self.num_maskmem-1 + # We also allow taking the memory frame non-consecutively (with r>1), in which case + # we take (self.num_maskmem - 2) frames among every r-th frames plus the last frame. + r = self.memory_temporal_stride_for_eval + for t_pos in range(1, self.num_maskmem): + t_rel = self.num_maskmem - t_pos # how many frames before current frame + if t_rel == 1: + # for t_rel == 1, we take the last frame (regardless of r) + if not track_in_reverse: + # the frame immediately before this frame (i.e. frame_idx - 1) + prev_frame_idx = frame_idx - t_rel + else: + # the frame immediately after this frame (i.e. frame_idx + 1) + prev_frame_idx = frame_idx + t_rel + else: + # for t_rel >= 2, we take the memory frame from every r-th frames + if not track_in_reverse: + # first find the nearest frame among every r-th frames before this frame + # for r=1, this would be (frame_idx - 2) + prev_frame_idx = ((frame_idx - 2) // r) * r + # then seek further among every r-th frames + prev_frame_idx = prev_frame_idx - (t_rel - 2) * r + else: + # first find the nearest frame among every r-th frames after this frame + # for r=1, this would be (frame_idx + 2) + prev_frame_idx = -(-(frame_idx + 2) // r) * r + # then seek further among every r-th frames + prev_frame_idx = prev_frame_idx + (t_rel - 2) * r + out = output_dict["non_cond_frame_outputs"].get(prev_frame_idx, None) + if out is None: + # If an unselected conditioning frame is among the last (self.num_maskmem - 1) + # frames, we still attend to it as if it's a non-conditioning frame. + out = unselected_cond_outputs.get(prev_frame_idx, None) + t_pos_and_prevs.append((t_pos, out)) + + for t_pos, prev in t_pos_and_prevs: + if prev is None: + continue # skip padding frames + # "maskmem_features" might have been offloaded to CPU in demo use cases, + # so we load it back to GPU (it's a no-op if it's already on GPU). + feats = prev["maskmem_features"].to(device, non_blocking=True) + to_cat_memory.append(feats.flatten(2).permute(2, 0, 1)) + # Spatial positional encoding (it might have been offloaded to CPU in eval) + maskmem_enc = prev["maskmem_pos_enc"][-1].to(device) + maskmem_enc = maskmem_enc.flatten(2).permute(2, 0, 1) + # Temporal positional encoding + maskmem_enc = ( + maskmem_enc + self.maskmem_tpos_enc[self.num_maskmem - t_pos - 1] + ) + to_cat_memory_pos_embed.append(maskmem_enc) + + # Construct the list of past object pointers + if self.use_obj_ptrs_in_encoder: + max_obj_ptrs_in_encoder = min(num_frames, self.max_obj_ptrs_in_encoder) + # First add those object pointers from selected conditioning frames + # (optionally, only include object pointers in the past during evaluation) + if not self.training and self.only_obj_ptrs_in_the_past_for_eval: + ptr_cond_outputs = { + t: out + for t, out in selected_cond_outputs.items() + if (t >= frame_idx if track_in_reverse else t <= frame_idx) + } + else: + ptr_cond_outputs = selected_cond_outputs + pos_and_ptrs = [ + # Temporal pos encoding contains how far away each pointer is from current frame + (abs(frame_idx - t), out["obj_ptr"]) + for t, out in ptr_cond_outputs.items() + ] + # Add up to (max_obj_ptrs_in_encoder - 1) non-conditioning frames before current frame + for t_diff in range(1, max_obj_ptrs_in_encoder): + t = frame_idx + t_diff if track_in_reverse else frame_idx - t_diff + if t < 0 or (num_frames is not None and t >= num_frames): + break + out = output_dict["non_cond_frame_outputs"].get( + t, unselected_cond_outputs.get(t, None) + ) + if out is not None: + pos_and_ptrs.append((t_diff, out["obj_ptr"])) + # If we have at least one object pointer, add them to the across attention + if len(pos_and_ptrs) > 0: + pos_list, ptrs_list = zip(*pos_and_ptrs) + # stack object pointers along dim=0 into [ptr_seq_len, B, C] shape + obj_ptrs = torch.stack(ptrs_list, dim=0) + # a temporal positional embedding based on how far each object pointer is from + # the current frame (sine embedding normalized by the max pointer num). + if self.add_tpos_enc_to_obj_ptrs: + t_diff_max = max_obj_ptrs_in_encoder - 1 + tpos_dim = C if self.proj_tpos_enc_in_obj_ptrs else self.mem_dim + obj_pos = torch.tensor(pos_list, device=device) + obj_pos = get_1d_sine_pe(obj_pos / t_diff_max, dim=tpos_dim) + obj_pos = self.obj_ptr_tpos_proj(obj_pos) + obj_pos = obj_pos.unsqueeze(1).expand(-1, B, self.mem_dim) + else: + obj_pos = obj_ptrs.new_zeros(len(pos_list), B, self.mem_dim) + if self.mem_dim < C: + # split a pointer into (C // self.mem_dim) tokens for self.mem_dim < C + obj_ptrs = obj_ptrs.reshape( + -1, B, C // self.mem_dim, self.mem_dim + ) + obj_ptrs = obj_ptrs.permute(0, 2, 1, 3).flatten(0, 1) + obj_pos = obj_pos.repeat_interleave(C // self.mem_dim, dim=0) + to_cat_memory.append(obj_ptrs) + to_cat_memory_pos_embed.append(obj_pos) + num_obj_ptr_tokens = obj_ptrs.shape[0] + else: + num_obj_ptr_tokens = 0 + else: + # for initial conditioning frames, encode them without using any previous memory + if self.directly_add_no_mem_embed: + # directly add no-mem embedding (instead of using the transformer encoder) + pix_feat_with_mem = current_vision_feats[-1] + self.no_mem_embed + pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W) + return pix_feat_with_mem + + # Use a dummy token on the first frame (to avoid emtpy memory input to tranformer encoder) + to_cat_memory = [self.no_mem_embed.expand(1, B, self.mem_dim)] + to_cat_memory_pos_embed = [self.no_mem_pos_enc.expand(1, B, self.mem_dim)] + + # Step 2: Concatenate the memories and forward through the transformer encoder + memory = torch.cat(to_cat_memory, dim=0) + memory_pos_embed = torch.cat(to_cat_memory_pos_embed, dim=0) + + pix_feat_with_mem = self.memory_attention( + curr=current_vision_feats, + curr_pos=current_vision_pos_embeds, + memory=memory, + memory_pos=memory_pos_embed, + num_obj_ptr_tokens=num_obj_ptr_tokens, + ) + # reshape the output (HW)BC => BCHW + pix_feat_with_mem = pix_feat_with_mem.permute(1, 2, 0).view(B, C, H, W) + return pix_feat_with_mem + + def _encode_new_memory( + self, + current_vision_feats, + feat_sizes, + pred_masks_high_res, + is_mask_from_pts, + ): + """Encode the current image and its prediction into a memory feature.""" + B = current_vision_feats[-1].size(1) # batch size on this frame + C = self.hidden_dim + H, W = feat_sizes[-1] # top-level (lowest-resolution) feature size + # top-level feature, (HW)BC => BCHW + pix_feat = current_vision_feats[-1].permute(1, 2, 0).view(B, C, H, W) + if self.non_overlap_masks_for_mem_enc and not self.training: + # optionally, apply non-overlapping constraints to the masks (it's applied + # in the batch dimension and should only be used during eval, where all + # the objects come from the same video under batch size 1). + pred_masks_high_res = self._apply_non_overlapping_constraints( + pred_masks_high_res + ) + # scale the raw mask logits with a temperature before applying sigmoid + binarize = self.binarize_mask_from_pts_for_mem_enc and is_mask_from_pts + if binarize and not self.training: + mask_for_mem = (pred_masks_high_res > 0).float() + else: + # apply sigmoid on the raw mask logits to turn them into range (0, 1) + mask_for_mem = torch.sigmoid(pred_masks_high_res) + # apply scale and bias terms to the sigmoid probabilities + if self.sigmoid_scale_for_mem_enc != 1.0: + mask_for_mem = mask_for_mem * self.sigmoid_scale_for_mem_enc + if self.sigmoid_bias_for_mem_enc != 0.0: + mask_for_mem = mask_for_mem + self.sigmoid_bias_for_mem_enc + maskmem_out = self.memory_encoder( + pix_feat, mask_for_mem, skip_mask_sigmoid=True # sigmoid already applied + ) + maskmem_features = maskmem_out["vision_features"] + maskmem_pos_enc = maskmem_out["vision_pos_enc"] + + return maskmem_features, maskmem_pos_enc + + def track_step( + self, + frame_idx, + is_init_cond_frame, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + point_inputs, + mask_inputs, + output_dict, + num_frames, + track_in_reverse=False, # tracking in reverse time order (for demo usage) + # Whether to run the memory encoder on the predicted masks. Sometimes we might want + # to skip the memory encoder with `run_mem_encoder=False`. For example, + # in demo we might call `track_step` multiple times for each user click, + # and only encode the memory when the user finalizes their clicks. And in ablation + # settings like SAM training on static images, we don't need the memory encoder. + run_mem_encoder=True, + # The previously predicted SAM mask logits (which can be fed together with new clicks in demo). + prev_sam_mask_logits=None, + ): + current_out = {"point_inputs": point_inputs, "mask_inputs": mask_inputs} + # High-resolution feature maps for the SAM head, reshape (HW)BC => BCHW + if len(current_vision_feats) > 1: + high_res_features = [ + x.permute(1, 2, 0).view(x.size(1), x.size(2), *s) + for x, s in zip(current_vision_feats[:-1], feat_sizes[:-1]) + ] + else: + high_res_features = None + if mask_inputs is not None and self.use_mask_input_as_output_without_sam: + # When use_mask_input_as_output_without_sam=True, we directly output the mask input + # (see it as a GT mask) without using a SAM prompt encoder + mask decoder. + pix_feat = current_vision_feats[-1].permute(1, 2, 0) + pix_feat = pix_feat.view(-1, self.hidden_dim, *feat_sizes[-1]) + sam_outputs = self._use_mask_as_output( + pix_feat, high_res_features, mask_inputs + ) + else: + # fused the visual feature with previous memory features in the memory bank + pix_feat_with_mem = self._prepare_memory_conditioned_features( + frame_idx=frame_idx, + is_init_cond_frame=is_init_cond_frame, + current_vision_feats=current_vision_feats[-1:], + current_vision_pos_embeds=current_vision_pos_embeds[-1:], + feat_sizes=feat_sizes[-1:], + output_dict=output_dict, + num_frames=num_frames, + track_in_reverse=track_in_reverse, + ) + # apply SAM-style segmentation head + # here we might feed previously predicted low-res SAM mask logits into the SAM mask decoder, + # e.g. in demo where such logits come from earlier interaction instead of correction sampling + # (in this case, any `mask_inputs` shouldn't reach here as they are sent to _use_mask_as_output instead) + if prev_sam_mask_logits is not None: + assert point_inputs is not None and mask_inputs is None + mask_inputs = prev_sam_mask_logits + multimask_output = self._use_multimask(is_init_cond_frame, point_inputs) + sam_outputs = self._forward_sam_heads( + backbone_features=pix_feat_with_mem, + point_inputs=point_inputs, + mask_inputs=mask_inputs, + high_res_features=high_res_features, + multimask_output=multimask_output, + ) + ( + _, + _, + _, + low_res_masks, + high_res_masks, + obj_ptr, + _, + ) = sam_outputs + + current_out["pred_masks"] = low_res_masks + current_out["pred_masks_high_res"] = high_res_masks + current_out["obj_ptr"] = obj_ptr + + # Finally run the memory encoder on the predicted mask to encode + # it into a new memory feature (that can be used in future frames) + if run_mem_encoder and self.num_maskmem > 0: + high_res_masks_for_mem_enc = high_res_masks + maskmem_features, maskmem_pos_enc = self._encode_new_memory( + current_vision_feats=current_vision_feats, + feat_sizes=feat_sizes, + pred_masks_high_res=high_res_masks_for_mem_enc, + is_mask_from_pts=(point_inputs is not None), + ) + current_out["maskmem_features"] = maskmem_features + current_out["maskmem_pos_enc"] = maskmem_pos_enc + else: + current_out["maskmem_features"] = None + current_out["maskmem_pos_enc"] = None + + return current_out + + def _use_multimask(self, is_init_cond_frame, point_inputs): + """Whether to use multimask output in the SAM head.""" + num_pts = 0 if point_inputs is None else point_inputs["point_labels"].size(1) + multimask_output = ( + self.multimask_output_in_sam + and (is_init_cond_frame or self.multimask_output_for_tracking) + and (self.multimask_min_pt_num <= num_pts <= self.multimask_max_pt_num) + ) + return multimask_output + + def _apply_non_overlapping_constraints(self, pred_masks): + """ + Apply non-overlapping constraints to the object scores in pred_masks. Here we + keep only the highest scoring object at each spatial location in pred_masks. + """ + batch_size = pred_masks.size(0) + if batch_size == 1: + return pred_masks + + device = pred_masks.device + # "max_obj_inds": object index of the object with the highest score at each location + max_obj_inds = torch.argmax(pred_masks, dim=0, keepdim=True) + # "batch_obj_inds": object index of each object slice (along dim 0) in `pred_masks` + batch_obj_inds = torch.arange(batch_size, device=device)[:, None, None, None] + keep = max_obj_inds == batch_obj_inds + # suppress overlapping regions' scores below -10.0 so that the foreground regions + # don't overlap (here sigmoid(-10.0)=4.5398e-05) + pred_masks = torch.where(keep, pred_masks, torch.clamp(pred_masks, max=-10.0)) + return pred_masks diff --git a/py/sam2/modeling/sam2_utils.py b/py/sam2/modeling/sam2_utils.py new file mode 100644 index 0000000..6d97059 --- /dev/null +++ b/py/sam2/modeling/sam2_utils.py @@ -0,0 +1,149 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + + +import copy + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def select_closest_cond_frames(frame_idx, cond_frame_outputs, max_cond_frame_num): + """ + Select up to `max_cond_frame_num` conditioning frames from `cond_frame_outputs` + that are temporally closest to the current frame at `frame_idx`. Here, we take + - a) the closest conditioning frame before `frame_idx` (if any); + - b) the closest conditioning frame after `frame_idx` (if any); + - c) any other temporally closest conditioning frames until reaching a total + of `max_cond_frame_num` conditioning frames. + + Outputs: + - selected_outputs: selected items (keys & values) from `cond_frame_outputs`. + - unselected_outputs: items (keys & values) not selected in `cond_frame_outputs`. + """ + if max_cond_frame_num == -1 or len(cond_frame_outputs) <= max_cond_frame_num: + selected_outputs = cond_frame_outputs + unselected_outputs = {} + else: + assert max_cond_frame_num >= 2, "we should allow using 2+ conditioning frames" + selected_outputs = {} + + # the closest conditioning frame before `frame_idx` (if any) + idx_before = max((t for t in cond_frame_outputs if t < frame_idx), default=None) + if idx_before is not None: + selected_outputs[idx_before] = cond_frame_outputs[idx_before] + + # the closest conditioning frame after `frame_idx` (if any) + idx_after = min((t for t in cond_frame_outputs if t >= frame_idx), default=None) + if idx_after is not None: + selected_outputs[idx_after] = cond_frame_outputs[idx_after] + + # add other temporally closest conditioning frames until reaching a total + # of `max_cond_frame_num` conditioning frames. + num_remain = max_cond_frame_num - len(selected_outputs) + inds_remain = sorted( + (t for t in cond_frame_outputs if t not in selected_outputs), + key=lambda x: abs(x - frame_idx), + )[:num_remain] + selected_outputs.update((t, cond_frame_outputs[t]) for t in inds_remain) + unselected_outputs = { + t: v for t, v in cond_frame_outputs.items() if t not in selected_outputs + } + + return selected_outputs, unselected_outputs + + +def get_1d_sine_pe(pos_inds, dim, temperature=10000): + """ + Get 1D sine positional embedding as in the original Transformer paper. + """ + pe_dim = dim // 2 + dim_t = torch.arange(pe_dim, dtype=torch.float32, device=pos_inds.device) + dim_t = temperature ** (2 * (dim_t // 2) / pe_dim) + + pos_embed = pos_inds.unsqueeze(-1) / dim_t + pos_embed = torch.cat([pos_embed.sin(), pos_embed.cos()], dim=-1) + return pos_embed + + +def get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") + + +def get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + +class DropPath(nn.Module): + # adapted from https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py + def __init__(self, drop_prob=0.0, scale_by_keep=True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + if self.drop_prob == 0.0 or not self.training: + return x + keep_prob = 1 - self.drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and self.scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +# Lightly adapted from +# https://github.com/facebookresearch/MaskFormer/blob/main/mask_former/modeling/transformer/transformer_predictor.py # noqa +class MLP(nn.Module): + def __init__( + self, + input_dim: int, + hidden_dim: int, + output_dim: int, + num_layers: int, + activation: nn.Module = nn.ReLU, + sigmoid_output: bool = False, + ) -> None: + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList( + nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]) + ) + self.sigmoid_output = sigmoid_output + self.act = activation() + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = self.act(layer(x)) if i < self.num_layers - 1 else layer(x) + if self.sigmoid_output: + x = F.sigmoid(x) + return x + + +# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa +# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa +class LayerNorm2d(nn.Module): + def __init__(self, num_channels: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(num_channels)) + self.bias = nn.Parameter(torch.zeros(num_channels)) + self.eps = eps + + def forward(self, x: torch.Tensor) -> torch.Tensor: + u = x.mean(1, keepdim=True) + s = (x - u).pow(2).mean(1, keepdim=True) + x = (x - u) / torch.sqrt(s + self.eps) + x = self.weight[:, None, None] * x + self.bias[:, None, None] + return x diff --git a/py/sam2/sam2_configs/__init__.py b/py/sam2/sam2_configs/__init__.py new file mode 100644 index 0000000..5277f46 --- /dev/null +++ b/py/sam2/sam2_configs/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/py/sam2/sam2_configs/sam2_hiera_b+.yaml b/py/sam2/sam2_configs/sam2_hiera_b+.yaml new file mode 100644 index 0000000..a03b8f7 --- /dev/null +++ b/py/sam2/sam2_configs/sam2_hiera_b+.yaml @@ -0,0 +1,116 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 112 + num_heads: 2 + stages: [2, 3, 16, 3] + global_att_blocks: [12, 16, 20] + window_pos_embed_bkg_spatial_size: [14, 14] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [896, 448, 224, 112] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: false + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/py/sam2/sam2_configs/sam2_hiera_l.yaml b/py/sam2/sam2_configs/sam2_hiera_l.yaml new file mode 100644 index 0000000..918667f --- /dev/null +++ b/py/sam2/sam2_configs/sam2_hiera_l.yaml @@ -0,0 +1,117 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 144 + num_heads: 2 + stages: [2, 6, 36, 4] + global_att_blocks: [23, 33, 43] + window_pos_embed_bkg_spatial_size: [7, 7] + window_spec: [8, 4, 16, 8] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [1152, 576, 288, 144] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: false + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/py/sam2/sam2_configs/sam2_hiera_s.yaml b/py/sam2/sam2_configs/sam2_hiera_s.yaml new file mode 100644 index 0000000..26e5d4d --- /dev/null +++ b/py/sam2/sam2_configs/sam2_hiera_s.yaml @@ -0,0 +1,116 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 96 + num_heads: 1 + stages: [1, 2, 11, 2] + global_att_blocks: [7, 10, 13] + window_pos_embed_bkg_spatial_size: [7, 7] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [768, 384, 192, 96] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: false + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + compile_image_encoder: False diff --git a/py/sam2/sam2_configs/sam2_hiera_t.yaml b/py/sam2/sam2_configs/sam2_hiera_t.yaml new file mode 100644 index 0000000..a62c903 --- /dev/null +++ b/py/sam2/sam2_configs/sam2_hiera_t.yaml @@ -0,0 +1,118 @@ +# @package _global_ + +# Model +model: + _target_: sam2.modeling.sam2_base.SAM2Base + image_encoder: + _target_: sam2.modeling.backbones.image_encoder.ImageEncoder + scalp: 1 + trunk: + _target_: sam2.modeling.backbones.hieradet.Hiera + embed_dim: 96 + num_heads: 1 + stages: [1, 2, 7, 2] + global_att_blocks: [5, 7, 9] + window_pos_embed_bkg_spatial_size: [7, 7] + neck: + _target_: sam2.modeling.backbones.image_encoder.FpnNeck + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 256 + normalize: true + scale: null + temperature: 10000 + d_model: 256 + backbone_channel_list: [768, 384, 192, 96] + fpn_top_down_levels: [2, 3] # output level 0 and 1 directly use the backbone features + fpn_interp_model: nearest + + memory_attention: + _target_: sam2.modeling.memory_attention.MemoryAttention + d_model: 256 + pos_enc_at_input: true + layer: + _target_: sam2.modeling.memory_attention.MemoryAttentionLayer + activation: relu + dim_feedforward: 2048 + dropout: 0.1 + pos_enc_at_attn: false + self_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + d_model: 256 + pos_enc_at_cross_attn_keys: true + pos_enc_at_cross_attn_queries: false + cross_attention: + _target_: sam2.modeling.sam.transformer.RoPEAttention + rope_theta: 10000.0 + feat_sizes: [32, 32] + rope_k_repeat: True + embedding_dim: 256 + num_heads: 1 + downsample_rate: 1 + dropout: 0.1 + kv_in_dim: 64 + num_layers: 4 + + memory_encoder: + _target_: sam2.modeling.memory_encoder.MemoryEncoder + out_dim: 64 + position_encoding: + _target_: sam2.modeling.position_encoding.PositionEmbeddingSine + num_pos_feats: 64 + normalize: true + scale: null + temperature: 10000 + mask_downsampler: + _target_: sam2.modeling.memory_encoder.MaskDownSampler + kernel_size: 3 + stride: 2 + padding: 1 + fuser: + _target_: sam2.modeling.memory_encoder.Fuser + layer: + _target_: sam2.modeling.memory_encoder.CXBlock + dim: 256 + kernel_size: 7 + padding: 3 + layer_scale_init_value: 1e-6 + use_dwconv: True # depth-wise convs + num_layers: 2 + + num_maskmem: 7 + image_size: 1024 + # apply scaled sigmoid on mask logits for memory encoder, and directly feed input mask as output mask + # SAM decoder + sigmoid_scale_for_mem_enc: 20.0 + sigmoid_bias_for_mem_enc: -10.0 + use_mask_input_as_output_without_sam: true + # Memory + directly_add_no_mem_embed: true + # use high-resolution feature map in the SAM mask decoder + use_high_res_features_in_sam: true + # output 3 masks on the first click on initial conditioning frames + multimask_output_in_sam: true + # SAM heads + iou_prediction_use_sigmoid: True + # cross-attend to object pointers from other frames (based on SAM output tokens) in the encoder + use_obj_ptrs_in_encoder: true + add_tpos_enc_to_obj_ptrs: false + only_obj_ptrs_in_the_past_for_eval: true + # object occlusion prediction + pred_obj_scores: true + pred_obj_scores_mlp: true + fixed_no_obj_ptr: true + # multimask tracking settings + multimask_output_for_tracking: true + use_multimask_token_for_obj_ptr: true + multimask_min_pt_num: 0 + multimask_max_pt_num: 1 + use_mlp_for_obj_ptr_proj: true + # Compilation flag + # HieraT does not currently support compilation, should always be set to False + compile_image_encoder: False diff --git a/py/sam2/sam2_image_predictor.py b/py/sam2/sam2_image_predictor.py new file mode 100644 index 0000000..271a0f0 --- /dev/null +++ b/py/sam2/sam2_image_predictor.py @@ -0,0 +1,446 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import logging + +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from PIL.Image import Image + +from ..sam2.modeling.sam2_base import SAM2Base + +from ..sam2.utils.transforms import SAM2Transforms + + +class SAM2ImagePredictor: + def __init__( + self, + sam_model: SAM2Base, + mask_threshold=0.0, + max_hole_area=0.0, + max_sprinkle_area=0.0, + ) -> None: + """ + Uses SAM-2 to calculate the image embedding for an image, and then + allow repeated, efficient mask prediction given prompts. + + Arguments: + sam_model (Sam-2): The model to use for mask prediction. + mask_threshold (float): The threshold to use when converting mask logits + to binary masks. Masks are thresholded at 0 by default. + fill_hole_area (int): If fill_hole_area > 0, we fill small holes in up to + the maximum area of fill_hole_area in low_res_masks. + """ + super().__init__() + self.model = sam_model + self._transforms = SAM2Transforms( + resolution=self.model.image_size, + mask_threshold=mask_threshold, + max_hole_area=max_hole_area, + max_sprinkle_area=max_sprinkle_area, + ) + + # Predictor state + self._is_image_set = False + self._features = None + self._orig_hw = None + # Whether the predictor is set for single image or a batch of images + self._is_batch = False + + # Predictor config + self.mask_threshold = mask_threshold + + # Spatial dim for backbone feature maps + self._bb_feat_sizes = [ + (256, 256), + (128, 128), + (64, 64), + ] + + @torch.no_grad() + def set_image( + self, + image: Union[np.ndarray, Image], + ) -> None: + """ + Calculates the image embeddings for the provided image, allowing + masks to be predicted with the 'predict' method. + + Arguments: + image (np.ndarray or PIL Image): The input image to embed in RGB format. The image should be in HWC format if np.ndarray, or WHC format if PIL Image + with pixel values in [0, 255]. + image_format (str): The color format of the image, in ['RGB', 'BGR']. + """ + self.reset_predictor() + # Transform the image to the form expected by the model + if isinstance(image, np.ndarray): + #logging.info("For numpy array image, we assume (HxWxC) format") + self._orig_hw = [image.shape[:2]] + elif isinstance(image, Image): + w, h = image.size + self._orig_hw = [(h, w)] + else: + raise NotImplementedError("Image format not supported") + + input_image = self._transforms(image) + input_image = input_image[None, ...].to(self.device) + + assert ( + len(input_image.shape) == 4 and input_image.shape[1] == 3 + ), f"input_image must be of size 1x3xHxW, got {input_image.shape}" + #logging.info("Computing image embeddings for the provided image...") + backbone_out = self.model.forward_image(input_image) + _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out) + # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos + if self.model.directly_add_no_mem_embed: + vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed + + feats = [ + feat.permute(1, 2, 0).view(1, -1, *feat_size) + for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1]) + ][::-1] + self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]} + self._is_image_set = True + #logging.info("Image embeddings computed.") + + @torch.no_grad() + def set_image_batch( + self, + image_list: List[Union[np.ndarray]], + ) -> None: + """ + Calculates the image embeddings for the provided image batch, allowing + masks to be predicted with the 'predict_batch' method. + + Arguments: + image_list (List[np.ndarray]): The input images to embed in RGB format. The image should be in HWC format if np.ndarray + with pixel values in [0, 255]. + """ + self.reset_predictor() + assert isinstance(image_list, list) + self._orig_hw = [] + for image in image_list: + assert isinstance( + image, np.ndarray + ), "Images are expected to be an np.ndarray in RGB format, and of shape HWC" + self._orig_hw.append(image.shape[:2]) + # Transform the image to the form expected by the model + img_batch = self._transforms.forward_batch(image_list) + img_batch = img_batch.to(self.device) + batch_size = img_batch.shape[0] + assert ( + len(img_batch.shape) == 4 and img_batch.shape[1] == 3 + ), f"img_batch must be of size Bx3xHxW, got {img_batch.shape}" + logging.info("Computing image embeddings for the provided images...") + backbone_out = self.model.forward_image(img_batch) + _, vision_feats, _, _ = self.model._prepare_backbone_features(backbone_out) + # Add no_mem_embed, which is added to the lowest rest feat. map during training on videos + if self.model.directly_add_no_mem_embed: + vision_feats[-1] = vision_feats[-1] + self.model.no_mem_embed + + feats = [ + feat.permute(1, 2, 0).view(batch_size, -1, *feat_size) + for feat, feat_size in zip(vision_feats[::-1], self._bb_feat_sizes[::-1]) + ][::-1] + self._features = {"image_embed": feats[-1], "high_res_feats": feats[:-1]} + self._is_image_set = True + self._is_batch = True + logging.info("Image embeddings computed.") + + def predict_batch( + self, + point_coords_batch: List[np.ndarray] = None, + point_labels_batch: List[np.ndarray] = None, + box_batch: List[np.ndarray] = None, + mask_input_batch: List[np.ndarray] = None, + multimask_output: bool = True, + return_logits: bool = False, + normalize_coords=True, + ) -> Tuple[List[np.ndarray], List[np.ndarray], List[np.ndarray]]: + """This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple images. + It returns a tupele of lists of masks, ious, and low_res_masks_logits. + """ + assert self._is_batch, "This function should only be used when in batched mode" + if not self._is_image_set: + raise RuntimeError( + "An image must be set with .set_image_batch(...) before mask prediction." + ) + num_images = len(self._features["image_embed"]) + all_masks = [] + all_ious = [] + all_low_res_masks = [] + for img_idx in range(num_images): + # Transform input prompts + point_coords = ( + point_coords_batch[img_idx] if point_coords_batch is not None else None + ) + point_labels = ( + point_labels_batch[img_idx] if point_labels_batch is not None else None + ) + box = box_batch[img_idx] if box_batch is not None else None + mask_input = ( + mask_input_batch[img_idx] if mask_input_batch is not None else None + ) + mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts( + point_coords, + point_labels, + box, + mask_input, + normalize_coords, + img_idx=img_idx, + ) + masks, iou_predictions, low_res_masks = self._predict( + unnorm_coords, + labels, + unnorm_box, + mask_input, + multimask_output, + return_logits=return_logits, + img_idx=img_idx, + ) + masks_np = masks.squeeze(0).float().detach().cpu().numpy() + iou_predictions_np = ( + iou_predictions.squeeze(0).float().detach().cpu().numpy() + ) + low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy() + all_masks.append(masks_np) + all_ious.append(iou_predictions_np) + all_low_res_masks.append(low_res_masks_np) + + return all_masks, all_ious, all_low_res_masks + + def predict( + self, + point_coords: Optional[np.ndarray] = None, + point_labels: Optional[np.ndarray] = None, + box: Optional[np.ndarray] = None, + mask_input: Optional[np.ndarray] = None, + multimask_output: bool = True, + return_logits: bool = False, + normalize_coords=True, + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Predict masks for the given input prompts, using the currently set image. + + Arguments: + point_coords (np.ndarray or None): A Nx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (np.ndarray or None): A length N array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + box (np.ndarray or None): A length 4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form 1xHxW, where + for SAM, H=W=256. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + normalize_coords (bool): If true, the point coordinates will be normalized to the range [0,1] and point_coords is expected to be wrt. image dimensions. + + Returns: + (np.ndarray): The output masks in CxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (np.ndarray): An array of length C containing the model's + predictions for the quality of each mask. + (np.ndarray): An array of shape CxHxW, where C is the number + of masks and H=W=256. These low resolution logits can be passed to + a subsequent iteration as mask input. + """ + if not self._is_image_set: + raise RuntimeError( + "An image must be set with .set_image(...) before mask prediction." + ) + + # Transform input prompts + + mask_input, unnorm_coords, labels, unnorm_box = self._prep_prompts( + point_coords, point_labels, box, mask_input, normalize_coords + ) + + masks, iou_predictions, low_res_masks = self._predict( + unnorm_coords, + labels, + unnorm_box, + mask_input, + multimask_output, + return_logits=return_logits, + ) + + masks_np = masks.squeeze(0).float().detach().cpu().numpy() + iou_predictions_np = iou_predictions.squeeze(0).float().detach().cpu().numpy() + low_res_masks_np = low_res_masks.squeeze(0).float().detach().cpu().numpy() + return masks_np, iou_predictions_np, low_res_masks_np + + def _prep_prompts( + self, point_coords, point_labels, box, mask_logits, normalize_coords, img_idx=-1 + ): + + unnorm_coords, labels, unnorm_box, mask_input = None, None, None, None + if point_coords is not None: + assert ( + point_labels is not None + ), "point_labels must be supplied if point_coords is supplied." + point_coords = torch.as_tensor( + point_coords, dtype=torch.float, device=self.device + ) + unnorm_coords = self._transforms.transform_coords( + point_coords, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx] + ) + labels = torch.as_tensor(point_labels, dtype=torch.int, device=self.device) + if len(unnorm_coords.shape) == 2: + unnorm_coords, labels = unnorm_coords[None, ...], labels[None, ...] + if box is not None: + box = torch.as_tensor(box, dtype=torch.float, device=self.device) + unnorm_box = self._transforms.transform_boxes( + box, normalize=normalize_coords, orig_hw=self._orig_hw[img_idx] + ) # Bx2x2 + if mask_logits is not None: + mask_input = torch.as_tensor( + mask_logits, dtype=torch.float, device=self.device + ) + if len(mask_input.shape) == 3: + mask_input = mask_input[None, :, :, :] + return mask_input, unnorm_coords, labels, unnorm_box + + @torch.no_grad() + def _predict( + self, + point_coords: Optional[torch.Tensor], + point_labels: Optional[torch.Tensor], + boxes: Optional[torch.Tensor] = None, + mask_input: Optional[torch.Tensor] = None, + multimask_output: bool = True, + return_logits: bool = False, + img_idx: int = -1, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """ + Predict masks for the given input prompts, using the currently set image. + Input prompts are batched torch tensors and are expected to already be + transformed to the input frame using SAM2Transforms. + + Arguments: + point_coords (torch.Tensor or None): A BxNx2 array of point prompts to the + model. Each point is in (X,Y) in pixels. + point_labels (torch.Tensor or None): A BxN array of labels for the + point prompts. 1 indicates a foreground point and 0 indicates a + background point. + boxes (np.ndarray or None): A Bx4 array given a box prompt to the + model, in XYXY format. + mask_input (np.ndarray): A low resolution mask input to the model, typically + coming from a previous prediction iteration. Has form Bx1xHxW, where + for SAM, H=W=256. Masks returned by a previous iteration of the + predict method do not need further transformation. + multimask_output (bool): If true, the model will return three masks. + For ambiguous input prompts (such as a single click), this will often + produce better masks than a single prediction. If only a single + mask is needed, the model's predicted quality score can be used + to select the best mask. For non-ambiguous prompts, such as multiple + input prompts, multimask_output=False can give better results. + return_logits (bool): If true, returns un-thresholded masks logits + instead of a binary mask. + + Returns: + (torch.Tensor): The output masks in BxCxHxW format, where C is the + number of masks, and (H, W) is the original image size. + (torch.Tensor): An array of shape BxC containing the model's + predictions for the quality of each mask. + (torch.Tensor): An array of shape BxCxHxW, where C is the number + of masks and H=W=256. These low res logits can be passed to + a subsequent iteration as mask input. + """ + if not self._is_image_set: + raise RuntimeError( + "An image must be set with .set_image(...) before mask prediction." + ) + + if point_coords is not None: + concat_points = (point_coords, point_labels) + else: + concat_points = None + + # Embed prompts + if boxes is not None: + box_coords = boxes.reshape(-1, 2, 2) + box_labels = torch.tensor([[2, 3]], dtype=torch.int, device=boxes.device) + box_labels = box_labels.repeat(boxes.size(0), 1) + # we merge "boxes" and "points" into a single "concat_points" input (where + # boxes are added at the beginning) to sam_prompt_encoder + if concat_points is not None: + concat_coords = torch.cat([box_coords, concat_points[0]], dim=1) + concat_labels = torch.cat([box_labels, concat_points[1]], dim=1) + concat_points = (concat_coords, concat_labels) + else: + concat_points = (box_coords, box_labels) + + sparse_embeddings, dense_embeddings = self.model.sam_prompt_encoder( + points=concat_points, + boxes=None, + masks=mask_input, + ) + + # Predict masks + batched_mode = ( + concat_points is not None and concat_points[0].shape[0] > 1 + ) # multi object prediction + high_res_features = [ + feat_level[img_idx].unsqueeze(0) + for feat_level in self._features["high_res_feats"] + ] + low_res_masks, iou_predictions, _, _ = self.model.sam_mask_decoder( + image_embeddings=self._features["image_embed"][img_idx].unsqueeze(0), + image_pe=self.model.sam_prompt_encoder.get_dense_pe(), + sparse_prompt_embeddings=sparse_embeddings, + dense_prompt_embeddings=dense_embeddings, + multimask_output=multimask_output, + repeat_image=batched_mode, + high_res_features=high_res_features, + ) + + # Upscale the masks to the original image resolution + masks = self._transforms.postprocess_masks( + low_res_masks, self._orig_hw[img_idx] + ) + low_res_masks = torch.clamp(low_res_masks, -32.0, 32.0) + if not return_logits: + masks = masks > self.mask_threshold + + return masks, iou_predictions, low_res_masks + + def get_image_embedding(self) -> torch.Tensor: + """ + Returns the image embeddings for the currently set image, with + shape 1xCxHxW, where C is the embedding dimension and (H,W) are + the embedding spatial dimension of SAM (typically C=256, H=W=64). + """ + if not self._is_image_set: + raise RuntimeError( + "An image must be set with .set_image(...) to generate an embedding." + ) + assert ( + self._features is not None + ), "Features must exist if an image has been set." + return self._features["image_embed"] + + @property + def device(self) -> torch.device: + return self.model.device + + def reset_predictor(self) -> None: + """ + Resets the image embeddings and other state variables. + """ + self._is_image_set = False + self._features = None + self._orig_hw = None + self._is_batch = False diff --git a/py/sam2/sam2_video_predictor.py b/py/sam2/sam2_video_predictor.py new file mode 100644 index 0000000..b5df87d --- /dev/null +++ b/py/sam2/sam2_video_predictor.py @@ -0,0 +1,906 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from collections import OrderedDict + +import torch +import numpy as np +from tqdm import tqdm + +from ..sam2.modeling.sam2_base import NO_OBJ_SCORE, SAM2Base +from ..sam2.utils.misc import concat_points, fill_holes_in_mask_scores, load_video_frames + + +class SAM2VideoPredictor(SAM2Base): + """The predictor class to handle user interactions and manage inference states.""" + + def __init__( + self, + fill_hole_area=0, + # whether to apply non-overlapping constraints on the output object masks + non_overlap_masks=False, + # whether to clear non-conditioning memory of the surrounding frames (which may contain outdated information) after adding correction clicks; + # note that this would only apply to *single-object tracking* unless `clear_non_cond_mem_for_multi_obj` is also set to True) + clear_non_cond_mem_around_input=False, + # whether to also clear non-conditioning memory of the surrounding frames (only effective when `clear_non_cond_mem_around_input` is True). + clear_non_cond_mem_for_multi_obj=False, + **kwargs, + ): + super().__init__(**kwargs) + self.fill_hole_area = fill_hole_area + self.non_overlap_masks = non_overlap_masks + self.clear_non_cond_mem_around_input = clear_non_cond_mem_around_input + self.clear_non_cond_mem_for_multi_obj = clear_non_cond_mem_for_multi_obj + + @torch.inference_mode() + def init_state( + self, + images, + video_height, + video_width, + device='cuda', + offload_video_to_cpu=False, + offload_state_to_cpu=False, + async_loading_frames=False, + ): + """Initialize a inference state.""" + # images, video_height, video_width = load_video_frames( + # video_path=video_path, + # image_size=self.image_size, + # offload_video_to_cpu=offload_video_to_cpu, + # async_loading_frames=async_loading_frames, + # ) + inference_state = {} + inference_state["images"] = images + inference_state["num_frames"] = len(images) + # whether to offload the video frames to CPU memory + # turning on this option saves the GPU memory with only a very small overhead + inference_state["offload_video_to_cpu"] = offload_video_to_cpu + # whether to offload the inference state to CPU memory + # turning on this option saves the GPU memory at the cost of a lower tracking fps + # (e.g. in a test case of 768x768 model, fps dropped from 27 to 24 when tracking one object + # and from 24 to 21 when tracking two objects) + inference_state["offload_state_to_cpu"] = offload_state_to_cpu + # the original video height and width, used for resizing final output scores + inference_state["video_height"] = video_height + inference_state["video_width"] = video_width + inference_state["device"] = torch.device(device) + if offload_state_to_cpu: + inference_state["storage_device"] = torch.device("cpu") + else: + inference_state["storage_device"] = torch.device(device) + # inputs on each frame + inference_state["point_inputs_per_obj"] = {} + inference_state["mask_inputs_per_obj"] = {} + # visual features on a small number of recently visited frames for quick interactions + inference_state["cached_features"] = {} + # values that don't change across frames (so we only need to hold one copy of them) + inference_state["constants"] = {} + # mapping between client-side object id and model-side object index + inference_state["obj_id_to_idx"] = OrderedDict() + inference_state["obj_idx_to_id"] = OrderedDict() + inference_state["obj_ids"] = [] + # A storage to hold the model's tracking results and states on each frame + inference_state["output_dict"] = { + "cond_frame_outputs": {}, # dict containing {frame_idx: } + "non_cond_frame_outputs": {}, # dict containing {frame_idx: } + } + # Slice (view) of each object tracking results, sharing the same memory with "output_dict" + inference_state["output_dict_per_obj"] = {} + # A temporary storage to hold new outputs when user interact with a frame + # to add clicks or mask (it's merged into "output_dict" before propagation starts) + inference_state["temp_output_dict_per_obj"] = {} + # Frames that already holds consolidated outputs from click or mask inputs + # (we directly use their consolidated outputs during tracking) + inference_state["consolidated_frame_inds"] = { + "cond_frame_outputs": set(), # set containing frame indices + "non_cond_frame_outputs": set(), # set containing frame indices + } + # metadata for each tracking frame (e.g. which direction it's tracked) + inference_state["tracking_has_started"] = False + inference_state["frames_already_tracked"] = {} + # Warm up the visual backbone and cache the image feature on frame 0 + self._get_image_feature(inference_state, frame_idx=0, batch_size=1) + return inference_state + + def _obj_id_to_idx(self, inference_state, obj_id): + """Map client-side object id to model-side object index.""" + obj_idx = inference_state["obj_id_to_idx"].get(obj_id, None) + if obj_idx is not None: + return obj_idx + + # This is a new object id not sent to the server before. We only allow adding + # new objects *before* the tracking starts. + allow_new_object = not inference_state["tracking_has_started"] + if allow_new_object: + # get the next object slot + obj_idx = len(inference_state["obj_id_to_idx"]) + inference_state["obj_id_to_idx"][obj_id] = obj_idx + inference_state["obj_idx_to_id"][obj_idx] = obj_id + inference_state["obj_ids"] = list(inference_state["obj_id_to_idx"]) + # set up input and output structures for this object + inference_state["point_inputs_per_obj"][obj_idx] = {} + inference_state["mask_inputs_per_obj"][obj_idx] = {} + inference_state["output_dict_per_obj"][obj_idx] = { + "cond_frame_outputs": {}, # dict containing {frame_idx: } + "non_cond_frame_outputs": {}, # dict containing {frame_idx: } + } + inference_state["temp_output_dict_per_obj"][obj_idx] = { + "cond_frame_outputs": {}, # dict containing {frame_idx: } + "non_cond_frame_outputs": {}, # dict containing {frame_idx: } + } + return obj_idx + else: + raise RuntimeError( + f"Cannot add new object id {obj_id} after tracking starts. " + f"All existing object ids: {inference_state['obj_ids']}. " + f"Please call 'reset_state' to restart from scratch." + ) + + def _obj_idx_to_id(self, inference_state, obj_idx): + """Map model-side object index to client-side object id.""" + return inference_state["obj_idx_to_id"][obj_idx] + + def _get_obj_num(self, inference_state): + """Get the total number of unique object ids received so far in this session.""" + return len(inference_state["obj_idx_to_id"]) + + @torch.inference_mode() + def add_new_points( + self, + inference_state, + frame_idx, + obj_id, + points, + labels, + clear_old_points=True, + normalize_coords=True, + ): + """Add new points to a frame.""" + obj_idx = self._obj_id_to_idx(inference_state, obj_id) + point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx] + mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx] + + if not isinstance(points, torch.Tensor): + if isinstance(points, list) and all(isinstance(p, np.ndarray) for p in points): + points = np.array(points) + points = torch.tensor(points, dtype=torch.float32) + + if not isinstance(labels, torch.Tensor): + if isinstance(labels, list) and all(isinstance(l, np.ndarray) for l in labels): + labels = np.array(labels) + labels = torch.tensor(labels, dtype=torch.int32) + if points.dim() == 2: + points = points.unsqueeze(0) # add batch dimension + if labels.dim() == 1: + labels = labels.unsqueeze(0) # add batch dimension + if normalize_coords: + video_H = inference_state["video_height"] + video_W = inference_state["video_width"] + points = points / torch.tensor([video_W, video_H]).to(points.device) + # scale the (normalized) coordinates by the model's internal image size + points = points * self.image_size + points = points.to(inference_state["device"]) + labels = labels.to(inference_state["device"]) + + if not clear_old_points: + point_inputs = point_inputs_per_frame.get(frame_idx, None) + else: + point_inputs = None + point_inputs = concat_points(point_inputs, points, labels) + + point_inputs_per_frame[frame_idx] = point_inputs + mask_inputs_per_frame.pop(frame_idx, None) + # If this frame hasn't been tracked before, we treat it as an initial conditioning + # frame, meaning that the inputs points are to generate segments on this frame without + # using any memory from other frames, like in SAM. Otherwise (if it has been tracked), + # the input points will be used to correct the already tracked masks. + is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"] + # whether to track in reverse time order + if is_init_cond_frame: + reverse = False + else: + reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"] + obj_output_dict = inference_state["output_dict_per_obj"][obj_idx] + obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx] + # Add a frame to conditioning output if it's an initial conditioning frame or + # if the model sees all frames receiving clicks/mask as conditioning frames. + is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond + storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs" + + # Get any previously predicted mask logits on this object and feed it along with + # the new clicks into the SAM mask decoder. + prev_sam_mask_logits = None + # lookup temporary output dict first, which contains the most recent output + # (if not found, then lookup conditioning and non-conditioning frame output) + prev_out = obj_temp_output_dict[storage_key].get(frame_idx) + if prev_out is None: + prev_out = obj_output_dict["cond_frame_outputs"].get(frame_idx) + if prev_out is None: + prev_out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx) + + if prev_out is not None and prev_out["pred_masks"] is not None: + prev_sam_mask_logits = prev_out["pred_masks"].to(inference_state["device"],non_blocking=True) + # Clamp the scale of prev_sam_mask_logits to avoid rare numerical issues. + prev_sam_mask_logits = torch.clamp(prev_sam_mask_logits, -32.0, 32.0) + current_out, _ = self._run_single_frame_inference( + inference_state=inference_state, + output_dict=obj_output_dict, # run on the slice of a single object + frame_idx=frame_idx, + batch_size=1, # run on the slice of a single object + is_init_cond_frame=is_init_cond_frame, + point_inputs=point_inputs, + mask_inputs=None, + reverse=reverse, + # Skip the memory encoder when adding clicks or mask. We execute the memory encoder + # at the beginning of `propagate_in_video` (after user finalize their clicks). This + # allows us to enforce non-overlapping constraints on all objects before encoding + # them into memory. + run_mem_encoder=False, + prev_sam_mask_logits=prev_sam_mask_logits, + ) + # Add the output to the output dict (to be used as future memory) + obj_temp_output_dict[storage_key][frame_idx] = current_out + + # Resize the output mask to the original video resolution + obj_ids = inference_state["obj_ids"] + consolidated_out = self._consolidate_temp_output_across_obj( + inference_state, + frame_idx, + is_cond=is_cond, + run_mem_encoder=False, + consolidate_at_video_res=True, + ) + _, video_res_masks = self._get_orig_video_res_output( + inference_state, consolidated_out["pred_masks_video_res"] + ) + return frame_idx, obj_ids, video_res_masks + + @torch.inference_mode() + def add_new_mask( + self, + inference_state, + frame_idx, + obj_id, + mask, + ): + """Add new mask to a frame.""" + obj_idx = self._obj_id_to_idx(inference_state, obj_id) + point_inputs_per_frame = inference_state["point_inputs_per_obj"][obj_idx] + mask_inputs_per_frame = inference_state["mask_inputs_per_obj"][obj_idx] + + if not isinstance(mask, torch.Tensor): + mask = torch.tensor(mask, dtype=torch.bool) + assert mask.dim() == 2 + mask_H, mask_W = mask.shape + mask_inputs_orig = mask[None, None] # add batch and channel dimension + mask_inputs_orig = mask_inputs_orig.float().to(inference_state["device"]) + + # resize the mask if it doesn't match the model's image size + if mask_H != self.image_size or mask_W != self.image_size: + mask_inputs = torch.nn.functional.interpolate( + mask_inputs_orig, + size=(self.image_size, self.image_size), + align_corners=False, + mode="bilinear", + antialias=True, # use antialias for downsampling + ) + mask_inputs = (mask_inputs >= 0.5).float() + else: + mask_inputs = mask_inputs_orig + + mask_inputs_per_frame[frame_idx] = mask_inputs + point_inputs_per_frame.pop(frame_idx, None) + # If this frame hasn't been tracked before, we treat it as an initial conditioning + # frame, meaning that the inputs points are to generate segments on this frame without + # using any memory from other frames, like in SAM. Otherwise (if it has been tracked), + # the input points will be used to correct the already tracked masks. + is_init_cond_frame = frame_idx not in inference_state["frames_already_tracked"] + # whether to track in reverse time order + if is_init_cond_frame: + reverse = False + else: + reverse = inference_state["frames_already_tracked"][frame_idx]["reverse"] + obj_output_dict = inference_state["output_dict_per_obj"][obj_idx] + obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx] + # Add a frame to conditioning output if it's an initial conditioning frame or + # if the model sees all frames receiving clicks/mask as conditioning frames. + is_cond = is_init_cond_frame or self.add_all_frames_to_correct_as_cond + storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs" + + current_out, _ = self._run_single_frame_inference( + inference_state=inference_state, + output_dict=obj_output_dict, # run on the slice of a single object + frame_idx=frame_idx, + batch_size=1, # run on the slice of a single object + is_init_cond_frame=is_init_cond_frame, + point_inputs=None, + mask_inputs=mask_inputs, + reverse=reverse, + # Skip the memory encoder when adding clicks or mask. We execute the memory encoder + # at the beginning of `propagate_in_video` (after user finalize their clicks). This + # allows us to enforce non-overlapping constraints on all objects before encoding + # them into memory. + run_mem_encoder=False, + ) + # Add the output to the output dict (to be used as future memory) + obj_temp_output_dict[storage_key][frame_idx] = current_out + + # Resize the output mask to the original video resolution + obj_ids = inference_state["obj_ids"] + consolidated_out = self._consolidate_temp_output_across_obj( + inference_state, + frame_idx, + is_cond=is_cond, + run_mem_encoder=False, + consolidate_at_video_res=True, + ) + _, video_res_masks = self._get_orig_video_res_output( + inference_state, consolidated_out["pred_masks_video_res"] + ) + return frame_idx, obj_ids, video_res_masks + + def _get_orig_video_res_output(self, inference_state, any_res_masks): + """ + Resize the object scores to the original video resolution (video_res_masks) + and apply non-overlapping constraints for final output. + """ + device = inference_state["device"] + video_H = inference_state["video_height"] + video_W = inference_state["video_width"] + any_res_masks = any_res_masks.to(device, non_blocking=True) + if any_res_masks.shape[-2:] == (video_H, video_W): + video_res_masks = any_res_masks + else: + video_res_masks = torch.nn.functional.interpolate( + any_res_masks, + size=(video_H, video_W), + mode="bilinear", + align_corners=False, + ) + if self.non_overlap_masks: + video_res_masks = self._apply_non_overlapping_constraints(video_res_masks) + return any_res_masks, video_res_masks + + def _consolidate_temp_output_across_obj( + self, + inference_state, + frame_idx, + is_cond, + run_mem_encoder, + consolidate_at_video_res=False, + ): + """ + Consolidate the per-object temporary outputs in `temp_output_dict_per_obj` on + a frame into a single output for all objects, including + 1) fill any missing objects either from `output_dict_per_obj` (if they exist in + `output_dict_per_obj` for this frame) or leave them as placeholder values + (if they don't exist in `output_dict_per_obj` for this frame); + 2) if specified, rerun memory encoder after apply non-overlapping constraints + on the object scores. + """ + batch_size = self._get_obj_num(inference_state) + storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs" + # Optionally, we allow consolidating the temporary outputs at the original + # video resolution (to provide a better editing experience for mask prompts). + if consolidate_at_video_res: + assert not run_mem_encoder, "memory encoder cannot run at video resolution" + consolidated_H = inference_state["video_height"] + consolidated_W = inference_state["video_width"] + consolidated_mask_key = "pred_masks_video_res" + else: + consolidated_H = consolidated_W = self.image_size // 4 + consolidated_mask_key = "pred_masks" + + # Initialize `consolidated_out`. Its "maskmem_features" and "maskmem_pos_enc" + # will be added when rerunning the memory encoder after applying non-overlapping + # constraints to object scores. Its "pred_masks" are prefilled with a large + # negative value (NO_OBJ_SCORE) to represent missing objects. + consolidated_out = { + "maskmem_features": None, + "maskmem_pos_enc": None, + consolidated_mask_key: torch.full( + size=(batch_size, 1, consolidated_H, consolidated_W), + fill_value=NO_OBJ_SCORE, + dtype=torch.float32, + device=inference_state["storage_device"], + ), + "obj_ptr": torch.full( + size=(batch_size, self.hidden_dim), + fill_value=NO_OBJ_SCORE, + dtype=torch.float32, + device=inference_state["device"], + ), + } + empty_mask_ptr = None + for obj_idx in range(batch_size): + obj_temp_output_dict = inference_state["temp_output_dict_per_obj"][obj_idx] + obj_output_dict = inference_state["output_dict_per_obj"][obj_idx] + out = obj_temp_output_dict[storage_key].get(frame_idx, None) + # If the object doesn't appear in "temp_output_dict_per_obj" on this frame, + # we fall back and look up its previous output in "output_dict_per_obj". + # We look up both "cond_frame_outputs" and "non_cond_frame_outputs" in + # "output_dict_per_obj" to find a previous output for this object. + if out is None: + out = obj_output_dict["cond_frame_outputs"].get(frame_idx, None) + if out is None: + out = obj_output_dict["non_cond_frame_outputs"].get(frame_idx, None) + # If the object doesn't appear in "output_dict_per_obj" either, we skip it + # and leave its mask scores to the default scores (i.e. the NO_OBJ_SCORE + # placeholder above) and set its object pointer to be a dummy pointer. + if out is None: + # Fill in dummy object pointers for those objects without any inputs or + # tracking outcomes on this frame (only do it under `run_mem_encoder=True`, + # i.e. when we need to build the memory for tracking). + if run_mem_encoder: + if empty_mask_ptr is None: + empty_mask_ptr = self._get_empty_mask_ptr( + inference_state, frame_idx + ) + # fill object pointer with a dummy pointer (based on an empty mask) + consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = empty_mask_ptr + continue + # Add the temporary object output mask to consolidated output mask + obj_mask = out["pred_masks"] + consolidated_pred_masks = consolidated_out[consolidated_mask_key] + if obj_mask.shape[-2:] == consolidated_pred_masks.shape[-2:]: + consolidated_pred_masks[obj_idx : obj_idx + 1] = obj_mask + else: + # Resize first if temporary object mask has a different resolution + resized_obj_mask = torch.nn.functional.interpolate( + obj_mask, + size=consolidated_pred_masks.shape[-2:], + mode="bilinear", + align_corners=False, + ) + consolidated_pred_masks[obj_idx : obj_idx + 1] = resized_obj_mask + consolidated_out["obj_ptr"][obj_idx : obj_idx + 1] = out["obj_ptr"] + + # Optionally, apply non-overlapping constraints on the consolidated scores + # and rerun the memory encoder + if run_mem_encoder: + device = inference_state["device"] + high_res_masks = torch.nn.functional.interpolate( + consolidated_out["pred_masks"].to(device, non_blocking=True), + size=(self.image_size, self.image_size), + mode="bilinear", + align_corners=False, + ) + if self.non_overlap_masks_for_mem_enc: + high_res_masks = self._apply_non_overlapping_constraints(high_res_masks) + maskmem_features, maskmem_pos_enc = self._run_memory_encoder( + inference_state=inference_state, + frame_idx=frame_idx, + batch_size=batch_size, + high_res_masks=high_res_masks, + is_mask_from_pts=True, # these frames are what the user interacted with + ) + consolidated_out["maskmem_features"] = maskmem_features + consolidated_out["maskmem_pos_enc"] = maskmem_pos_enc + + return consolidated_out + + def _get_empty_mask_ptr(self, inference_state, frame_idx): + """Get a dummy object pointer based on an empty mask on the current frame.""" + # A dummy (empty) mask with a single object + batch_size = 1 + mask_inputs = torch.zeros( + (batch_size, 1, self.image_size, self.image_size), + dtype=torch.float32, + device=inference_state["device"], + ) + + # Retrieve correct image features + ( + _, + _, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + ) = self._get_image_feature(inference_state, frame_idx, batch_size) + + # Feed the empty mask and image feature above to get a dummy object pointer + current_out = self.track_step( + frame_idx=frame_idx, + is_init_cond_frame=True, + current_vision_feats=current_vision_feats, + current_vision_pos_embeds=current_vision_pos_embeds, + feat_sizes=feat_sizes, + point_inputs=None, + mask_inputs=mask_inputs, + output_dict={}, + num_frames=inference_state["num_frames"], + track_in_reverse=False, + run_mem_encoder=False, + prev_sam_mask_logits=None, + ) + return current_out["obj_ptr"] + + @torch.inference_mode() + def propagate_in_video_preflight(self, inference_state): + """Prepare inference_state and consolidate temporary outputs before tracking.""" + # Tracking has started and we don't allow adding new objects until session is reset. + inference_state["tracking_has_started"] = True + batch_size = self._get_obj_num(inference_state) + + # Consolidate per-object temporary outputs in "temp_output_dict_per_obj" and + # add them into "output_dict". + temp_output_dict_per_obj = inference_state["temp_output_dict_per_obj"] + output_dict = inference_state["output_dict"] + # "consolidated_frame_inds" contains indices of those frames where consolidated + # temporary outputs have been added (either in this call or any previous calls + # to `propagate_in_video_preflight`). + consolidated_frame_inds = inference_state["consolidated_frame_inds"] + for is_cond in [False, True]: + # Separately consolidate conditioning and non-conditioning temp outptus + storage_key = "cond_frame_outputs" if is_cond else "non_cond_frame_outputs" + # Find all the frames that contain temporary outputs for any objects + # (these should be the frames that have just received clicks for mask inputs + # via `add_new_points` or `add_new_mask`) + temp_frame_inds = set() + for obj_temp_output_dict in temp_output_dict_per_obj.values(): + temp_frame_inds.update(obj_temp_output_dict[storage_key].keys()) + consolidated_frame_inds[storage_key].update(temp_frame_inds) + # consolidate the temprary output across all objects on this frame + for frame_idx in temp_frame_inds: + consolidated_out = self._consolidate_temp_output_across_obj( + inference_state, frame_idx, is_cond=is_cond, run_mem_encoder=True + ) + # merge them into "output_dict" and also create per-object slices + output_dict[storage_key][frame_idx] = consolidated_out + self._add_output_per_object( + inference_state, frame_idx, consolidated_out, storage_key + ) + clear_non_cond_mem = self.clear_non_cond_mem_around_input and ( + self.clear_non_cond_mem_for_multi_obj or batch_size <= 1 + ) + if clear_non_cond_mem: + # clear non-conditioning memory of the surrounding frames + self._clear_non_cond_mem_around_input(inference_state, frame_idx) + + # clear temporary outputs in `temp_output_dict_per_obj` + for obj_temp_output_dict in temp_output_dict_per_obj.values(): + obj_temp_output_dict[storage_key].clear() + + # edge case: if an output is added to "cond_frame_outputs", we remove any prior + # output on the same frame in "non_cond_frame_outputs" + for frame_idx in output_dict["cond_frame_outputs"]: + output_dict["non_cond_frame_outputs"].pop(frame_idx, None) + for obj_output_dict in inference_state["output_dict_per_obj"].values(): + for frame_idx in obj_output_dict["cond_frame_outputs"]: + obj_output_dict["non_cond_frame_outputs"].pop(frame_idx, None) + for frame_idx in consolidated_frame_inds["cond_frame_outputs"]: + assert frame_idx in output_dict["cond_frame_outputs"] + consolidated_frame_inds["non_cond_frame_outputs"].discard(frame_idx) + + # Make sure that the frame indices in "consolidated_frame_inds" are exactly those frames + # with either points or mask inputs (which should be true under a correct workflow). + all_consolidated_frame_inds = ( + consolidated_frame_inds["cond_frame_outputs"] + | consolidated_frame_inds["non_cond_frame_outputs"] + ) + input_frames_inds = set() + for point_inputs_per_frame in inference_state["point_inputs_per_obj"].values(): + input_frames_inds.update(point_inputs_per_frame.keys()) + for mask_inputs_per_frame in inference_state["mask_inputs_per_obj"].values(): + input_frames_inds.update(mask_inputs_per_frame.keys()) + assert all_consolidated_frame_inds == input_frames_inds + + @torch.inference_mode() + def propagate_in_video( + self, + inference_state, + start_frame_idx=None, + max_frame_num_to_track=None, + reverse=False, + ): + """Propagate the input points across frames to track in the entire video.""" + self.propagate_in_video_preflight(inference_state) + + output_dict = inference_state["output_dict"] + consolidated_frame_inds = inference_state["consolidated_frame_inds"] + obj_ids = inference_state["obj_ids"] + num_frames = inference_state["num_frames"] + batch_size = self._get_obj_num(inference_state) + if len(output_dict["cond_frame_outputs"]) == 0: + raise RuntimeError("No points are provided; please add points first") + clear_non_cond_mem = self.clear_non_cond_mem_around_input and ( + self.clear_non_cond_mem_for_multi_obj or batch_size <= 1 + ) + + # set start index, end index, and processing order + if start_frame_idx is None: + # default: start from the earliest frame with input points + start_frame_idx = min(output_dict["cond_frame_outputs"]) + if max_frame_num_to_track is None: + # default: track all the frames in the video + max_frame_num_to_track = num_frames + if reverse: + end_frame_idx = max(start_frame_idx - max_frame_num_to_track, 0) + if start_frame_idx > 0: + processing_order = range(start_frame_idx, end_frame_idx - 1, -1) + else: + processing_order = [] # skip reverse tracking if starting from frame 0 + else: + end_frame_idx = min( + start_frame_idx + max_frame_num_to_track, num_frames - 1 + ) + processing_order = range(start_frame_idx, end_frame_idx + 1) + + for frame_idx in tqdm(processing_order, desc="propagate in video"): + # We skip those frames already in consolidated outputs (these are frames + # that received input clicks or mask). Note that we cannot directly run + # batched forward on them via `_run_single_frame_inference` because the + # number of clicks on each object might be different. + if frame_idx in consolidated_frame_inds["cond_frame_outputs"]: + storage_key = "cond_frame_outputs" + current_out = output_dict[storage_key][frame_idx] + pred_masks = current_out["pred_masks"] + if clear_non_cond_mem: + # clear non-conditioning memory of the surrounding frames + self._clear_non_cond_mem_around_input(inference_state, frame_idx) + elif frame_idx in consolidated_frame_inds["non_cond_frame_outputs"]: + storage_key = "non_cond_frame_outputs" + current_out = output_dict[storage_key][frame_idx] + pred_masks = current_out["pred_masks"] + else: + storage_key = "non_cond_frame_outputs" + current_out, pred_masks = self._run_single_frame_inference( + inference_state=inference_state, + output_dict=output_dict, + frame_idx=frame_idx, + batch_size=batch_size, + is_init_cond_frame=False, + point_inputs=None, + mask_inputs=None, + reverse=reverse, + run_mem_encoder=True, + ) + output_dict[storage_key][frame_idx] = current_out + # Create slices of per-object outputs for subsequent interaction with each + # individual object after tracking. + self._add_output_per_object( + inference_state, frame_idx, current_out, storage_key + ) + inference_state["frames_already_tracked"][frame_idx] = {"reverse": reverse} + + # Resize the output mask to the original video resolution (we directly use + # the mask scores on GPU for output to avoid any CPU conversion in between) + _, video_res_masks = self._get_orig_video_res_output( + inference_state, pred_masks + ) + yield frame_idx, obj_ids, video_res_masks + + def _add_output_per_object( + self, inference_state, frame_idx, current_out, storage_key + ): + """ + Split a multi-object output into per-object output slices and add them into + `output_dict_per_obj`. The resulting slices share the same tensor storage. + """ + maskmem_features = current_out["maskmem_features"] + assert maskmem_features is None or isinstance(maskmem_features, torch.Tensor) + + maskmem_pos_enc = current_out["maskmem_pos_enc"] + assert maskmem_pos_enc is None or isinstance(maskmem_pos_enc, list) + + output_dict_per_obj = inference_state["output_dict_per_obj"] + for obj_idx, obj_output_dict in output_dict_per_obj.items(): + obj_slice = slice(obj_idx, obj_idx + 1) + obj_out = { + "maskmem_features": None, + "maskmem_pos_enc": None, + "pred_masks": current_out["pred_masks"][obj_slice], + "obj_ptr": current_out["obj_ptr"][obj_slice], + } + if maskmem_features is not None: + obj_out["maskmem_features"] = maskmem_features[obj_slice] + if maskmem_pos_enc is not None: + obj_out["maskmem_pos_enc"] = [x[obj_slice] for x in maskmem_pos_enc] + obj_output_dict[storage_key][frame_idx] = obj_out + + @torch.inference_mode() + def reset_state(self, inference_state): + """Remove all input points or mask in all frames throughout the video.""" + self._reset_tracking_results(inference_state) + # Remove all object ids + inference_state["obj_id_to_idx"].clear() + inference_state["obj_idx_to_id"].clear() + inference_state["obj_ids"].clear() + inference_state["point_inputs_per_obj"].clear() + inference_state["mask_inputs_per_obj"].clear() + inference_state["output_dict_per_obj"].clear() + inference_state["temp_output_dict_per_obj"].clear() + + def _reset_tracking_results(self, inference_state): + """Reset all tracking inputs and results across the videos.""" + for v in inference_state["point_inputs_per_obj"].values(): + v.clear() + for v in inference_state["mask_inputs_per_obj"].values(): + v.clear() + for v in inference_state["output_dict_per_obj"].values(): + v["cond_frame_outputs"].clear() + v["non_cond_frame_outputs"].clear() + for v in inference_state["temp_output_dict_per_obj"].values(): + v["cond_frame_outputs"].clear() + v["non_cond_frame_outputs"].clear() + inference_state["output_dict"]["cond_frame_outputs"].clear() + inference_state["output_dict"]["non_cond_frame_outputs"].clear() + inference_state["consolidated_frame_inds"]["cond_frame_outputs"].clear() + inference_state["consolidated_frame_inds"]["non_cond_frame_outputs"].clear() + inference_state["tracking_has_started"] = False + inference_state["frames_already_tracked"].clear() + + def _get_image_feature(self, inference_state, frame_idx, batch_size): + """Compute the image features on a given frame.""" + # Look up in the cache first + image, backbone_out = inference_state["cached_features"].get( + frame_idx, (None, None) + ) + if backbone_out is None: + # Cache miss -- we will run inference on a single image + image = inference_state["images"][frame_idx].to(inference_state["device"]).float().unsqueeze(0) + backbone_out = self.forward_image(image) + # Cache the most recent frame's feature (for repeated interactions with + # a frame; we can use an LRU cache for more frames in the future). + inference_state["cached_features"] = {frame_idx: (image, backbone_out)} + + # expand the features to have the same dimension as the number of objects + expanded_image = image.expand(batch_size, -1, -1, -1) + expanded_backbone_out = { + "backbone_fpn": backbone_out["backbone_fpn"].copy(), + "vision_pos_enc": backbone_out["vision_pos_enc"].copy(), + } + for i, feat in enumerate(expanded_backbone_out["backbone_fpn"]): + expanded_backbone_out["backbone_fpn"][i] = feat.expand( + batch_size, -1, -1, -1 + ) + for i, pos in enumerate(expanded_backbone_out["vision_pos_enc"]): + pos = pos.expand(batch_size, -1, -1, -1) + expanded_backbone_out["vision_pos_enc"][i] = pos + + features = self._prepare_backbone_features(expanded_backbone_out) + features = (expanded_image,) + features + return features + + def _run_single_frame_inference( + self, + inference_state, + output_dict, + frame_idx, + batch_size, + is_init_cond_frame, + point_inputs, + mask_inputs, + reverse, + run_mem_encoder, + prev_sam_mask_logits=None, + ): + """Run tracking on a single frame based on current inputs and previous memory.""" + # Retrieve correct image features + ( + _, + _, + current_vision_feats, + current_vision_pos_embeds, + feat_sizes, + ) = self._get_image_feature(inference_state, frame_idx, batch_size) + + # point and mask should not appear as input simultaneously on the same frame + assert point_inputs is None or mask_inputs is None + current_out = self.track_step( + frame_idx=frame_idx, + is_init_cond_frame=is_init_cond_frame, + current_vision_feats=current_vision_feats, + current_vision_pos_embeds=current_vision_pos_embeds, + feat_sizes=feat_sizes, + point_inputs=point_inputs, + mask_inputs=mask_inputs, + output_dict=output_dict, + num_frames=inference_state["num_frames"], + track_in_reverse=reverse, + run_mem_encoder=run_mem_encoder, + prev_sam_mask_logits=prev_sam_mask_logits, + ) + + # optionally offload the output to CPU memory to save GPU space + storage_device = inference_state["storage_device"] + maskmem_features = current_out["maskmem_features"] + if maskmem_features is not None: + maskmem_features = maskmem_features.to(torch.bfloat16) + maskmem_features = maskmem_features.to(storage_device, non_blocking=True) + pred_masks_gpu = current_out["pred_masks"] + # potentially fill holes in the predicted masks + if self.fill_hole_area > 0: + pred_masks_gpu = fill_holes_in_mask_scores( + pred_masks_gpu, self.fill_hole_area + ) + pred_masks = pred_masks_gpu.to(storage_device, non_blocking=True) + # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it + maskmem_pos_enc = self._get_maskmem_pos_enc(inference_state, current_out) + # object pointer is a small tensor, so we always keep it on GPU memory for fast access + obj_ptr = current_out["obj_ptr"] + # make a compact version of this frame's output to reduce the state size + compact_current_out = { + "maskmem_features": maskmem_features, + "maskmem_pos_enc": maskmem_pos_enc, + "pred_masks": pred_masks, + "obj_ptr": obj_ptr, + } + return compact_current_out, pred_masks_gpu + + def _run_memory_encoder( + self, inference_state, frame_idx, batch_size, high_res_masks, is_mask_from_pts + ): + """ + Run the memory encoder on `high_res_masks`. This is usually after applying + non-overlapping constraints to object scores. Since their scores changed, their + memory also need to be computed again with the memory encoder. + """ + # Retrieve correct image features + _, _, current_vision_feats, _, feat_sizes = self._get_image_feature( + inference_state, frame_idx, batch_size + ) + maskmem_features, maskmem_pos_enc = self._encode_new_memory( + current_vision_feats=current_vision_feats, + feat_sizes=feat_sizes, + pred_masks_high_res=high_res_masks, + is_mask_from_pts=is_mask_from_pts, + ) + + # optionally offload the output to CPU memory to save GPU space + storage_device = inference_state["storage_device"] + maskmem_features = maskmem_features.to(torch.bfloat16) + maskmem_features = maskmem_features.to(storage_device, non_blocking=True) + # "maskmem_pos_enc" is the same across frames, so we only need to store one copy of it + maskmem_pos_enc = self._get_maskmem_pos_enc( + inference_state, {"maskmem_pos_enc": maskmem_pos_enc} + ) + return maskmem_features, maskmem_pos_enc + + def _get_maskmem_pos_enc(self, inference_state, current_out): + """ + `maskmem_pos_enc` is the same across frames and objects, so we cache it as + a constant in the inference session to reduce session storage size. + """ + model_constants = inference_state["constants"] + # "out_maskmem_pos_enc" should be either a list of tensors or None + out_maskmem_pos_enc = current_out["maskmem_pos_enc"] + if out_maskmem_pos_enc is not None: + if "maskmem_pos_enc" not in model_constants: + assert isinstance(out_maskmem_pos_enc, list) + # only take the slice for one object, since it's same across objects + maskmem_pos_enc = [x[0:1].clone() for x in out_maskmem_pos_enc] + model_constants["maskmem_pos_enc"] = maskmem_pos_enc + else: + maskmem_pos_enc = model_constants["maskmem_pos_enc"] + # expand the cached maskmem_pos_enc to the actual batch size + batch_size = out_maskmem_pos_enc[0].size(0) + expanded_maskmem_pos_enc = [ + x.expand(batch_size, -1, -1, -1) for x in maskmem_pos_enc + ] + else: + expanded_maskmem_pos_enc = None + return expanded_maskmem_pos_enc + + def _clear_non_cond_mem_around_input(self, inference_state, frame_idx): + """ + Remove the non-conditioning memory around the input frame. When users provide + correction clicks, the surrounding frames' non-conditioning memories can still + contain outdated object appearance information and could confuse the model. + + This method clears those non-conditioning memories surrounding the interacted + frame to avoid giving the model both old and new information about the object. + """ + r = self.memory_temporal_stride_for_eval + frame_idx_begin = frame_idx - r * self.num_maskmem + frame_idx_end = frame_idx + r * self.num_maskmem + output_dict = inference_state["output_dict"] + non_cond_frame_outputs = output_dict["non_cond_frame_outputs"] + for t in range(frame_idx_begin, frame_idx_end + 1): + non_cond_frame_outputs.pop(t, None) + for obj_output_dict in inference_state["output_dict_per_obj"].values(): + obj_output_dict["non_cond_frame_outputs"].pop(t, None) diff --git a/py/sam2/utils/__init__.py b/py/sam2/utils/__init__.py new file mode 100644 index 0000000..5277f46 --- /dev/null +++ b/py/sam2/utils/__init__.py @@ -0,0 +1,5 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. diff --git a/py/sam2/utils/amg.py b/py/sam2/utils/amg.py new file mode 100644 index 0000000..9868429 --- /dev/null +++ b/py/sam2/utils/amg.py @@ -0,0 +1,348 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math +from copy import deepcopy +from itertools import product +from typing import Any, Dict, Generator, ItemsView, List, Tuple + +import numpy as np +import torch + +# Very lightly adapted from https://github.com/facebookresearch/segment-anything/blob/main/segment_anything/utils/amg.py + + +class MaskData: + """ + A structure for storing masks and their related data in batched format. + Implements basic filtering and concatenation. + """ + + def __init__(self, **kwargs) -> None: + for v in kwargs.values(): + assert isinstance( + v, (list, np.ndarray, torch.Tensor) + ), "MaskData only supports list, numpy arrays, and torch tensors." + self._stats = dict(**kwargs) + + def __setitem__(self, key: str, item: Any) -> None: + assert isinstance( + item, (list, np.ndarray, torch.Tensor) + ), "MaskData only supports list, numpy arrays, and torch tensors." + self._stats[key] = item + + def __delitem__(self, key: str) -> None: + del self._stats[key] + + def __getitem__(self, key: str) -> Any: + return self._stats[key] + + def items(self) -> ItemsView[str, Any]: + return self._stats.items() + + def filter(self, keep: torch.Tensor) -> None: + for k, v in self._stats.items(): + if v is None: + self._stats[k] = None + elif isinstance(v, torch.Tensor): + self._stats[k] = v[torch.as_tensor(keep, device=v.device)] + elif isinstance(v, np.ndarray): + self._stats[k] = v[keep.detach().cpu().numpy()] + elif isinstance(v, list) and keep.dtype == torch.bool: + self._stats[k] = [a for i, a in enumerate(v) if keep[i]] + elif isinstance(v, list): + self._stats[k] = [v[i] for i in keep] + else: + raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") + + def cat(self, new_stats: "MaskData") -> None: + for k, v in new_stats.items(): + if k not in self._stats or self._stats[k] is None: + self._stats[k] = deepcopy(v) + elif isinstance(v, torch.Tensor): + self._stats[k] = torch.cat([self._stats[k], v], dim=0) + elif isinstance(v, np.ndarray): + self._stats[k] = np.concatenate([self._stats[k], v], axis=0) + elif isinstance(v, list): + self._stats[k] = self._stats[k] + deepcopy(v) + else: + raise TypeError(f"MaskData key {k} has an unsupported type {type(v)}.") + + def to_numpy(self) -> None: + for k, v in self._stats.items(): + if isinstance(v, torch.Tensor): + self._stats[k] = v.float().detach().cpu().numpy() + + +def is_box_near_crop_edge( + boxes: torch.Tensor, crop_box: List[int], orig_box: List[int], atol: float = 20.0 +) -> torch.Tensor: + """Filter masks at the edge of a crop, but not at the edge of the original image.""" + crop_box_torch = torch.as_tensor(crop_box, dtype=torch.float, device=boxes.device) + orig_box_torch = torch.as_tensor(orig_box, dtype=torch.float, device=boxes.device) + boxes = uncrop_boxes_xyxy(boxes, crop_box).float() + near_crop_edge = torch.isclose(boxes, crop_box_torch[None, :], atol=atol, rtol=0) + near_image_edge = torch.isclose(boxes, orig_box_torch[None, :], atol=atol, rtol=0) + near_crop_edge = torch.logical_and(near_crop_edge, ~near_image_edge) + return torch.any(near_crop_edge, dim=1) + + +def box_xyxy_to_xywh(box_xyxy: torch.Tensor) -> torch.Tensor: + box_xywh = deepcopy(box_xyxy) + box_xywh[2] = box_xywh[2] - box_xywh[0] + box_xywh[3] = box_xywh[3] - box_xywh[1] + return box_xywh + + +def batch_iterator(batch_size: int, *args) -> Generator[List[Any], None, None]: + assert len(args) > 0 and all( + len(a) == len(args[0]) for a in args + ), "Batched iteration must have inputs of all the same size." + n_batches = len(args[0]) // batch_size + int(len(args[0]) % batch_size != 0) + for b in range(n_batches): + yield [arg[b * batch_size : (b + 1) * batch_size] for arg in args] + + +def mask_to_rle_pytorch(tensor: torch.Tensor) -> List[Dict[str, Any]]: + """ + Encodes masks to an uncompressed RLE, in the format expected by + pycoco tools. + """ + # Put in fortran order and flatten h,w + b, h, w = tensor.shape + tensor = tensor.permute(0, 2, 1).flatten(1) + + # Compute change indices + diff = tensor[:, 1:] ^ tensor[:, :-1] + change_indices = diff.nonzero() + + # Encode run length + out = [] + for i in range(b): + cur_idxs = change_indices[change_indices[:, 0] == i, 1] + cur_idxs = torch.cat( + [ + torch.tensor([0], dtype=cur_idxs.dtype, device=cur_idxs.device), + cur_idxs + 1, + torch.tensor([h * w], dtype=cur_idxs.dtype, device=cur_idxs.device), + ] + ) + btw_idxs = cur_idxs[1:] - cur_idxs[:-1] + counts = [] if tensor[i, 0] == 0 else [0] + counts.extend(btw_idxs.detach().cpu().tolist()) + out.append({"size": [h, w], "counts": counts}) + return out + + +def rle_to_mask(rle: Dict[str, Any]) -> np.ndarray: + """Compute a binary mask from an uncompressed RLE.""" + h, w = rle["size"] + mask = np.empty(h * w, dtype=bool) + idx = 0 + parity = False + for count in rle["counts"]: + mask[idx : idx + count] = parity + idx += count + parity ^= True + mask = mask.reshape(w, h) + return mask.transpose() # Put in C order + + +def area_from_rle(rle: Dict[str, Any]) -> int: + return sum(rle["counts"][1::2]) + + +def calculate_stability_score( + masks: torch.Tensor, mask_threshold: float, threshold_offset: float +) -> torch.Tensor: + """ + Computes the stability score for a batch of masks. The stability + score is the IoU between the binary masks obtained by thresholding + the predicted mask logits at high and low values. + """ + # One mask is always contained inside the other. + # Save memory by preventing unnecessary cast to torch.int64 + intersections = ( + (masks > (mask_threshold + threshold_offset)) + .sum(-1, dtype=torch.int16) + .sum(-1, dtype=torch.int32) + ) + unions = ( + (masks > (mask_threshold - threshold_offset)) + .sum(-1, dtype=torch.int16) + .sum(-1, dtype=torch.int32) + ) + return intersections / unions + + +def build_point_grid(n_per_side: int) -> np.ndarray: + """Generates a 2D grid of points evenly spaced in [0,1]x[0,1].""" + offset = 1 / (2 * n_per_side) + points_one_side = np.linspace(offset, 1 - offset, n_per_side) + points_x = np.tile(points_one_side[None, :], (n_per_side, 1)) + points_y = np.tile(points_one_side[:, None], (1, n_per_side)) + points = np.stack([points_x, points_y], axis=-1).reshape(-1, 2) + return points + + +def build_all_layer_point_grids( + n_per_side: int, n_layers: int, scale_per_layer: int +) -> List[np.ndarray]: + """Generates point grids for all crop layers.""" + points_by_layer = [] + for i in range(n_layers + 1): + n_points = int(n_per_side / (scale_per_layer**i)) + points_by_layer.append(build_point_grid(n_points)) + return points_by_layer + + +def generate_crop_boxes( + im_size: Tuple[int, ...], n_layers: int, overlap_ratio: float +) -> Tuple[List[List[int]], List[int]]: + """ + Generates a list of crop boxes of different sizes. Each layer + has (2**i)**2 boxes for the ith layer. + """ + crop_boxes, layer_idxs = [], [] + im_h, im_w = im_size + short_side = min(im_h, im_w) + + # Original image + crop_boxes.append([0, 0, im_w, im_h]) + layer_idxs.append(0) + + def crop_len(orig_len, n_crops, overlap): + return int(math.ceil((overlap * (n_crops - 1) + orig_len) / n_crops)) + + for i_layer in range(n_layers): + n_crops_per_side = 2 ** (i_layer + 1) + overlap = int(overlap_ratio * short_side * (2 / n_crops_per_side)) + + crop_w = crop_len(im_w, n_crops_per_side, overlap) + crop_h = crop_len(im_h, n_crops_per_side, overlap) + + crop_box_x0 = [int((crop_w - overlap) * i) for i in range(n_crops_per_side)] + crop_box_y0 = [int((crop_h - overlap) * i) for i in range(n_crops_per_side)] + + # Crops in XYWH format + for x0, y0 in product(crop_box_x0, crop_box_y0): + box = [x0, y0, min(x0 + crop_w, im_w), min(y0 + crop_h, im_h)] + crop_boxes.append(box) + layer_idxs.append(i_layer + 1) + + return crop_boxes, layer_idxs + + +def uncrop_boxes_xyxy(boxes: torch.Tensor, crop_box: List[int]) -> torch.Tensor: + x0, y0, _, _ = crop_box + offset = torch.tensor([[x0, y0, x0, y0]], device=boxes.device) + # Check if boxes has a channel dimension + if len(boxes.shape) == 3: + offset = offset.unsqueeze(1) + return boxes + offset + + +def uncrop_points(points: torch.Tensor, crop_box: List[int]) -> torch.Tensor: + x0, y0, _, _ = crop_box + offset = torch.tensor([[x0, y0]], device=points.device) + # Check if points has a channel dimension + if len(points.shape) == 3: + offset = offset.unsqueeze(1) + return points + offset + + +def uncrop_masks( + masks: torch.Tensor, crop_box: List[int], orig_h: int, orig_w: int +) -> torch.Tensor: + x0, y0, x1, y1 = crop_box + if x0 == 0 and y0 == 0 and x1 == orig_w and y1 == orig_h: + return masks + # Coordinate transform masks + pad_x, pad_y = orig_w - (x1 - x0), orig_h - (y1 - y0) + pad = (x0, pad_x - x0, y0, pad_y - y0) + return torch.nn.functional.pad(masks, pad, value=0) + + +def remove_small_regions( + mask: np.ndarray, area_thresh: float, mode: str +) -> Tuple[np.ndarray, bool]: + """ + Removes small disconnected regions and holes in a mask. Returns the + mask and an indicator of if the mask has been modified. + """ + import cv2 # type: ignore + + assert mode in ["holes", "islands"] + correct_holes = mode == "holes" + working_mask = (correct_holes ^ mask).astype(np.uint8) + n_labels, regions, stats, _ = cv2.connectedComponentsWithStats(working_mask, 8) + sizes = stats[:, -1][1:] # Row 0 is background label + small_regions = [i + 1 for i, s in enumerate(sizes) if s < area_thresh] + if len(small_regions) == 0: + return mask, False + fill_labels = [0] + small_regions + if not correct_holes: + fill_labels = [i for i in range(n_labels) if i not in fill_labels] + # If every region is below threshold, keep largest + if len(fill_labels) == 0: + fill_labels = [int(np.argmax(sizes)) + 1] + mask = np.isin(regions, fill_labels) + return mask, True + + +def coco_encode_rle(uncompressed_rle: Dict[str, Any]) -> Dict[str, Any]: + from pycocotools import mask as mask_utils # type: ignore + + h, w = uncompressed_rle["size"] + rle = mask_utils.frPyObjects(uncompressed_rle, h, w) + rle["counts"] = rle["counts"].decode("utf-8") # Necessary to serialize with json + return rle + + +def batched_mask_to_box(masks: torch.Tensor) -> torch.Tensor: + """ + Calculates boxes in XYXY format around masks. Return [0,0,0,0] for + an empty mask. For input shape C1xC2x...xHxW, the output shape is C1xC2x...x4. + """ + # torch.max below raises an error on empty inputs, just skip in this case + if torch.numel(masks) == 0: + return torch.zeros(*masks.shape[:-2], 4, device=masks.device) + + # Normalize shape to CxHxW + shape = masks.shape + h, w = shape[-2:] + if len(shape) > 2: + masks = masks.flatten(0, -3) + else: + masks = masks.unsqueeze(0) + + # Get top and bottom edges + in_height, _ = torch.max(masks, dim=-1) + in_height_coords = in_height * torch.arange(h, device=in_height.device)[None, :] + bottom_edges, _ = torch.max(in_height_coords, dim=-1) + in_height_coords = in_height_coords + h * (~in_height) + top_edges, _ = torch.min(in_height_coords, dim=-1) + + # Get left and right edges + in_width, _ = torch.max(masks, dim=-2) + in_width_coords = in_width * torch.arange(w, device=in_width.device)[None, :] + right_edges, _ = torch.max(in_width_coords, dim=-1) + in_width_coords = in_width_coords + w * (~in_width) + left_edges, _ = torch.min(in_width_coords, dim=-1) + + # If the mask is empty the right edge will be to the left of the left edge. + # Replace these boxes with [0, 0, 0, 0] + empty_filter = (right_edges < left_edges) | (bottom_edges < top_edges) + out = torch.stack([left_edges, top_edges, right_edges, bottom_edges], dim=-1) + out = out * (~empty_filter).unsqueeze(-1) + + # Return to original shape + if len(shape) > 2: + out = out.reshape(*shape[:-2], 4) + else: + out = out[0] + + return out diff --git a/py/sam2/utils/misc.py b/py/sam2/utils/misc.py new file mode 100644 index 0000000..4487711 --- /dev/null +++ b/py/sam2/utils/misc.py @@ -0,0 +1,238 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import os +import warnings +from threading import Thread + +import numpy as np +import torch +from PIL import Image +from tqdm import tqdm +import platform + +def get_sdpa_settings(): + if torch.cuda.is_available(): + old_gpu = torch.cuda.get_device_properties(0).major < 7 + # only use Flash Attention on Ampere (8.0) or newer GPUs + use_flash_attn = torch.cuda.get_device_properties(0).major >= 8 and platform.system() == 'Linux' + if not use_flash_attn: + warnings.warn( + "Flash Attention is disabled as it requires a GPU with Ampere (8.0) CUDA capability.", + category=UserWarning, + stacklevel=2, + ) + # keep math kernel for PyTorch versions before 2.2 (Flash Attention v2 is only + # available on PyTorch 2.2+, while Flash Attention v1 cannot handle all cases) + pytorch_version = tuple(int(v) for v in torch.__version__.split(".")[:2]) + if pytorch_version < (2, 2): + warnings.warn( + f"You are using PyTorch {torch.__version__} without Flash Attention v2 support. " + "Consider upgrading to PyTorch 2.2+ for Flash Attention v2 (which could be faster).", + category=UserWarning, + stacklevel=2, + ) + math_kernel_on = pytorch_version < (2, 2) or not use_flash_attn + else: + old_gpu = True + use_flash_attn = False + math_kernel_on = True + + return old_gpu, use_flash_attn, math_kernel_on + + +def get_connected_components(mask): + """ + Get the connected components (8-connectivity) of binary masks of shape (N, 1, H, W). + + Inputs: + - mask: A binary mask tensor of shape (N, 1, H, W), where 1 is foreground and 0 is + background. + + Outputs: + - labels: A tensor of shape (N, 1, H, W) containing the connected component labels + for foreground pixels and 0 for background pixels. + - counts: A tensor of shape (N, 1, H, W) containing the area of the connected + components for foreground pixels and 0 for background pixels. + """ + from ...sam2 import _C + + return _C.get_connected_componnets(mask.to(torch.uint8).contiguous()) + + +def mask_to_box(masks: torch.Tensor): + """ + compute bounding box given an input mask + + Inputs: + - masks: [B, 1, H, W] boxes, dtype=torch.Tensor + + Returns: + - box_coords: [B, 1, 4], contains (x, y) coordinates of top left and bottom right box corners, dtype=torch.Tensor + """ + B, _, h, w = masks.shape + device = masks.device + xs = torch.arange(w, device=device, dtype=torch.int32) + ys = torch.arange(h, device=device, dtype=torch.int32) + grid_xs, grid_ys = torch.meshgrid(xs, ys, indexing="xy") + grid_xs = grid_xs[None, None, ...].expand(B, 1, h, w) + grid_ys = grid_ys[None, None, ...].expand(B, 1, h, w) + min_xs, _ = torch.min(torch.where(masks, grid_xs, w).flatten(-2), dim=-1) + max_xs, _ = torch.max(torch.where(masks, grid_xs, -1).flatten(-2), dim=-1) + min_ys, _ = torch.min(torch.where(masks, grid_ys, h).flatten(-2), dim=-1) + max_ys, _ = torch.max(torch.where(masks, grid_ys, -1).flatten(-2), dim=-1) + bbox_coords = torch.stack((min_xs, min_ys, max_xs, max_ys), dim=-1) + + return bbox_coords + + +def _load_img_as_tensor(img_path, image_size): + img_pil = Image.open(img_path) + img_np = np.array(img_pil.convert("RGB").resize((image_size, image_size))) + if img_np.dtype == np.uint8: # np.uint8 is expected for JPEG images + img_np = img_np / 255.0 + else: + raise RuntimeError(f"Unknown image dtype: {img_np.dtype} on {img_path}") + img = torch.from_numpy(img_np).permute(2, 0, 1) + video_width, video_height = img_pil.size # the original video size + return img, video_height, video_width + + +class AsyncVideoFrameLoader: + """ + A list of video frames to be load asynchronously without blocking session start. + """ + + def __init__(self, img_paths, image_size, offload_video_to_cpu, img_mean, img_std): + self.img_paths = img_paths + self.image_size = image_size + self.offload_video_to_cpu = offload_video_to_cpu + self.img_mean = img_mean + self.img_std = img_std + # items in `self._images` will be loaded asynchronously + self.images = [None] * len(img_paths) + # catch and raise any exceptions in the async loading thread + self.exception = None + # video_height and video_width be filled when loading the first image + self.video_height = None + self.video_width = None + + # load the first frame to fill video_height and video_width and also + # to cache it (since it's most likely where the user will click) + self.__getitem__(0) + + # load the rest of frames asynchronously without blocking the session start + def _load_frames(): + try: + for n in tqdm(range(len(self.images)), desc="frame loading (JPEG)"): + self.__getitem__(n) + except Exception as e: + self.exception = e + + self.thread = Thread(target=_load_frames, daemon=True) + self.thread.start() + + def __getitem__(self, index): + if self.exception is not None: + raise RuntimeError("Failure in frame loading thread") from self.exception + + img = self.images[index] + if img is not None: + return img + + img, video_height, video_width = _load_img_as_tensor( + self.img_paths[index], self.image_size + ) + self.video_height = video_height + self.video_width = video_width + # normalize by mean and std + img -= self.img_mean + img /= self.img_std + if not self.offload_video_to_cpu: + img = img.cuda(non_blocking=True) + self.images[index] = img + return img + + def __len__(self): + return len(self.images) + + +def load_video_frames( + video_path, + image_size, + offload_video_to_cpu, + img_mean=(0.485, 0.456, 0.406), + img_std=(0.229, 0.224, 0.225), + async_loading_frames=False, +): + """ + Load the video frames from a directory of JPEG files (".jpg" format). + + The frames are resized to image_size x image_size and are loaded to GPU if + `offload_video_to_cpu` is `False` and to CPU if `offload_video_to_cpu` is `True`. + + You can load a frame asynchronously by setting `async_loading_frames` to `True`. + """ + if isinstance(video_path, str) and os.path.isdir(video_path): + jpg_folder = video_path + else: + raise NotImplementedError("Only JPEG frames are supported at this moment") + + frame_names = [ + p + for p in os.listdir(jpg_folder) + if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"] + ] + frame_names.sort(key=lambda p: int(os.path.splitext(p)[0])) + num_frames = len(frame_names) + if num_frames == 0: + raise RuntimeError(f"no images found in {jpg_folder}") + img_paths = [os.path.join(jpg_folder, frame_name) for frame_name in frame_names] + img_mean = torch.tensor(img_mean, dtype=torch.float32)[:, None, None] + img_std = torch.tensor(img_std, dtype=torch.float32)[:, None, None] + + if async_loading_frames: + lazy_images = AsyncVideoFrameLoader( + img_paths, image_size, offload_video_to_cpu, img_mean, img_std + ) + return lazy_images, lazy_images.video_height, lazy_images.video_width + + images = torch.zeros(num_frames, 3, image_size, image_size, dtype=torch.float32) + for n, img_path in enumerate(tqdm(img_paths, desc="frame loading (JPEG)")): + images[n], video_height, video_width = _load_img_as_tensor(img_path, image_size) + if not offload_video_to_cpu: + images = images.cuda() + img_mean = img_mean.cuda() + img_std = img_std.cuda() + # normalize by mean and std + images -= img_mean + images /= img_std + return images, video_height, video_width + + +def fill_holes_in_mask_scores(mask, max_area): + """ + A post processor to fill small holes in mask scores with area under `max_area`. + """ + # Holes are those connected components in background with area <= self.max_area + # (background regions are those with mask scores <= 0) + assert max_area > 0, "max_area must be positive" + labels, areas = get_connected_components(mask <= 0) + is_hole = (labels > 0) & (areas <= max_area) + # We fill holes with a small positive mask score (0.1) to change them to foreground. + mask = torch.where(is_hole, 0.1, mask) + return mask + + +def concat_points(old_point_inputs, new_points, new_labels): + """Add new points and labels to previous point inputs (add at the end).""" + if old_point_inputs is None: + points, labels = new_points, new_labels + else: + points = torch.cat([old_point_inputs["point_coords"], new_points], dim=1) + labels = torch.cat([old_point_inputs["point_labels"], new_labels], dim=1) + + return {"point_coords": points, "point_labels": labels} diff --git a/py/sam2/utils/transforms.py b/py/sam2/utils/transforms.py new file mode 100644 index 0000000..428a569 --- /dev/null +++ b/py/sam2/utils/transforms.py @@ -0,0 +1,106 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.transforms import Normalize, Resize, ToTensor + + +class SAM2Transforms(nn.Module): + def __init__( + self, resolution, mask_threshold, max_hole_area=0.0, max_sprinkle_area=0.0 + ): + """ + Transforms for SAM2. + """ + super().__init__() + self.resolution = resolution + self.mask_threshold = mask_threshold + self.max_hole_area = max_hole_area + self.max_sprinkle_area = max_sprinkle_area + self.mean = [0.485, 0.456, 0.406] + self.std = [0.229, 0.224, 0.225] + self.to_tensor = ToTensor() + try: + self.transforms = torch.jit.script( + nn.Sequential( + Resize((self.resolution, self.resolution)), + Normalize(self.mean, self.std), + ) + ) + except Exception as e: + print(f"Failed to torch jit script transforms: {e}, falling back to normal transforms") + self.transforms = nn.Sequential( + Resize((self.resolution, self.resolution)), + Normalize(self.mean, self.std), + ) + + def __call__(self, x): + x = self.to_tensor(x) + return self.transforms(x) + + def forward_batch(self, img_list): + img_batch = [self.transforms(self.to_tensor(img)) for img in img_list] + img_batch = torch.stack(img_batch, dim=0) + return img_batch + + def transform_coords( + self, coords: torch.Tensor, normalize=False, orig_hw=None + ) -> torch.Tensor: + """ + Expects a torch tensor with length 2 in the last dimension. The coordinates can be in absolute image or normalized coordinates, + If the coords are in absolute image coordinates, normalize should be set to True and original image size is required. + + Returns + Un-normalized coordinates in the range of [0, 1] which is expected by the SAM2 model. + """ + if normalize: + assert orig_hw is not None + h, w = orig_hw + coords = coords.clone() + coords[..., 0] = coords[..., 0] / w + coords[..., 1] = coords[..., 1] / h + + coords = coords * self.resolution # unnormalize coords + return coords + + def transform_boxes( + self, boxes: torch.Tensor, normalize=False, orig_hw=None + ) -> torch.Tensor: + """ + Expects a tensor of shape Bx4. The coordinates can be in absolute image or normalized coordinates, + if the coords are in absolute image coordinates, normalize should be set to True and original image size is required. + """ + boxes = self.transform_coords(boxes.reshape(-1, 2, 2), normalize, orig_hw) + return boxes + + def postprocess_masks(self, masks: torch.Tensor, orig_hw) -> torch.Tensor: + """ + Perform PostProcessing on output masks. + """ + #from ...sam2.utils.misc import get_connected_components + + masks = masks.float() + # if self.max_hole_area > 0: + # # Holes are those connected components in background with area <= self.fill_hole_area + # # (background regions are those with mask scores <= self.mask_threshold) + # mask_flat = masks.flatten(0, 1).unsqueeze(1) # flatten as 1-channel image + # labels, areas = get_connected_components(mask_flat <= self.mask_threshold) + # is_hole = (labels > 0) & (areas <= self.max_hole_area) + # is_hole = is_hole.reshape_as(masks) + # # We fill holes with a small positive mask score (10.0) to change them to foreground. + # masks = torch.where(is_hole, self.mask_threshold + 10.0, masks) + + # if self.max_sprinkle_area > 0: + # labels, areas = get_connected_components(mask_flat > self.mask_threshold) + # is_hole = (labels > 0) & (areas <= self.max_sprinkle_area) + # is_hole = is_hole.reshape_as(masks) + # # We fill holes with negative mask score (-10.0) to change them to background. + # masks = torch.where(is_hole, self.mask_threshold - 10.0, masks) + + masks = F.interpolate(masks, orig_hw, mode="bilinear", align_corners=False) + return masks diff --git a/py/sam_2_ultrl.py b/py/sam_2_ultrl.py new file mode 100644 index 0000000..22875e3 --- /dev/null +++ b/py/sam_2_ultrl.py @@ -0,0 +1,628 @@ +import cv2 +import torch +import yaml +import comfy.model_management as mm +from comfy.utils import ProgressBar +from comfy.utils import load_torch_file +from .imagefunc import * + +def bboxes2coordinates(bboxes:list) -> list: + coordinates = [] + for bbox in bboxes: + coordinates.append(((bbox[0]+bbox[2]) // 2, (bbox[1]+bbox[3]) // 2)) + return coordinates +def load_model(model_path, model_cfg_path, segmentor, dtype, device): + from .sam2.modeling.sam2_base import SAM2Base + from .sam2.modeling.backbones.image_encoder import ImageEncoder + from .sam2.modeling.backbones.hieradet import Hiera + from .sam2.modeling.backbones.image_encoder import FpnNeck + from .sam2.modeling.position_encoding import PositionEmbeddingSine + from .sam2.modeling.memory_attention import MemoryAttention, MemoryAttentionLayer + from .sam2.modeling.sam.transformer import RoPEAttention + from .sam2.modeling.memory_encoder import MemoryEncoder, MaskDownSampler, Fuser, CXBlock + from .sam2.sam2_image_predictor import SAM2ImagePredictor + from .sam2.sam2_video_predictor import SAM2VideoPredictor + from .sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator + + # Load the YAML configuration + with open(model_cfg_path, 'r') as file: + config = yaml.safe_load(file) + + # Extract the model configuration + model_config = config['model'] + + # Instantiate the image encoder components + trunk_config = model_config['image_encoder']['trunk'] + neck_config = model_config['image_encoder']['neck'] + position_encoding_config = neck_config['position_encoding'] + + position_encoding = PositionEmbeddingSine( + num_pos_feats=position_encoding_config['num_pos_feats'], + normalize=position_encoding_config['normalize'], + scale=position_encoding_config['scale'], + temperature=position_encoding_config['temperature'] + ) + + neck = FpnNeck( + position_encoding=position_encoding, + d_model=neck_config['d_model'], + backbone_channel_list=neck_config['backbone_channel_list'], + fpn_top_down_levels=neck_config['fpn_top_down_levels'], + fpn_interp_model=neck_config['fpn_interp_model'] + ) + + trunk = Hiera( + embed_dim=trunk_config['embed_dim'], + num_heads=trunk_config['num_heads'], + stages=trunk_config['stages'], + global_att_blocks=trunk_config['global_att_blocks'], + window_pos_embed_bkg_spatial_size=trunk_config['window_pos_embed_bkg_spatial_size'] + + ) + + image_encoder = ImageEncoder( + scalp=model_config['image_encoder']['scalp'], + trunk=trunk, + neck=neck + ) + # Instantiate the memory attention components + memory_attention_layer_config = config['model']['memory_attention']['layer'] + self_attention_config = memory_attention_layer_config['self_attention'] + cross_attention_config = memory_attention_layer_config['cross_attention'] + + self_attention = RoPEAttention( + rope_theta=self_attention_config['rope_theta'], + feat_sizes=self_attention_config['feat_sizes'], + embedding_dim=self_attention_config['embedding_dim'], + num_heads=self_attention_config['num_heads'], + downsample_rate=self_attention_config['downsample_rate'], + dropout=self_attention_config['dropout'] + ) + + cross_attention = RoPEAttention( + rope_theta=cross_attention_config['rope_theta'], + feat_sizes=cross_attention_config['feat_sizes'], + rope_k_repeat=cross_attention_config['rope_k_repeat'], + embedding_dim=cross_attention_config['embedding_dim'], + num_heads=cross_attention_config['num_heads'], + downsample_rate=cross_attention_config['downsample_rate'], + dropout=cross_attention_config['dropout'], + kv_in_dim=cross_attention_config['kv_in_dim'] + ) + + memory_attention_layer = MemoryAttentionLayer( + activation=memory_attention_layer_config['activation'], + dim_feedforward=memory_attention_layer_config['dim_feedforward'], + dropout=memory_attention_layer_config['dropout'], + pos_enc_at_attn=memory_attention_layer_config['pos_enc_at_attn'], + self_attention=self_attention, + d_model=memory_attention_layer_config['d_model'], + pos_enc_at_cross_attn_keys=memory_attention_layer_config['pos_enc_at_cross_attn_keys'], + pos_enc_at_cross_attn_queries=memory_attention_layer_config['pos_enc_at_cross_attn_queries'], + cross_attention=cross_attention + ) + + memory_attention = MemoryAttention( + d_model=config['model']['memory_attention']['d_model'], + pos_enc_at_input=config['model']['memory_attention']['pos_enc_at_input'], + layer=memory_attention_layer, + num_layers=config['model']['memory_attention']['num_layers'] + ) + + # Instantiate the memory encoder components + memory_encoder_config = config['model']['memory_encoder'] + position_encoding_mem_enc_config = memory_encoder_config['position_encoding'] + mask_downsampler_config = memory_encoder_config['mask_downsampler'] + fuser_layer_config = memory_encoder_config['fuser']['layer'] + + position_encoding_mem_enc = PositionEmbeddingSine( + num_pos_feats=position_encoding_mem_enc_config['num_pos_feats'], + normalize=position_encoding_mem_enc_config['normalize'], + scale=position_encoding_mem_enc_config['scale'], + temperature=position_encoding_mem_enc_config['temperature'] + ) + + mask_downsampler = MaskDownSampler( + kernel_size=mask_downsampler_config['kernel_size'], + stride=mask_downsampler_config['stride'], + padding=mask_downsampler_config['padding'] + ) + + fuser_layer = CXBlock( + dim=fuser_layer_config['dim'], + kernel_size=fuser_layer_config['kernel_size'], + padding=fuser_layer_config['padding'], + layer_scale_init_value=float(fuser_layer_config['layer_scale_init_value']) + ) + fuser = Fuser( + num_layers=memory_encoder_config['fuser']['num_layers'], + layer=fuser_layer + ) + + memory_encoder = MemoryEncoder( + position_encoding=position_encoding_mem_enc, + mask_downsampler=mask_downsampler, + fuser=fuser, + out_dim=memory_encoder_config['out_dim'] + ) + + sam_mask_decoder_extra_args = { + "dynamic_multimask_via_stability": True, + "dynamic_multimask_stability_delta": 0.05, + "dynamic_multimask_stability_thresh": 0.98, + } + + def initialize_model(model_class, model_config, segmentor, image_encoder, memory_attention, memory_encoder, sam_mask_decoder_extra_args, dtype, device): + return model_class( + image_encoder=image_encoder, + memory_attention=memory_attention, + memory_encoder=memory_encoder, + sam_mask_decoder_extra_args=sam_mask_decoder_extra_args, + num_maskmem=model_config['num_maskmem'], + image_size=model_config['image_size'], + sigmoid_scale_for_mem_enc=model_config['sigmoid_scale_for_mem_enc'], + sigmoid_bias_for_mem_enc=model_config['sigmoid_bias_for_mem_enc'], + use_mask_input_as_output_without_sam=model_config['use_mask_input_as_output_without_sam'], + directly_add_no_mem_embed=model_config['directly_add_no_mem_embed'], + use_high_res_features_in_sam=model_config['use_high_res_features_in_sam'], + multimask_output_in_sam=model_config['multimask_output_in_sam'], + iou_prediction_use_sigmoid=model_config['iou_prediction_use_sigmoid'], + use_obj_ptrs_in_encoder=model_config['use_obj_ptrs_in_encoder'], + add_tpos_enc_to_obj_ptrs=model_config['add_tpos_enc_to_obj_ptrs'], + only_obj_ptrs_in_the_past_for_eval=model_config['only_obj_ptrs_in_the_past_for_eval'], + pred_obj_scores=model_config['pred_obj_scores'], + pred_obj_scores_mlp=model_config['pred_obj_scores_mlp'], + fixed_no_obj_ptr=model_config['fixed_no_obj_ptr'], + multimask_output_for_tracking=model_config['multimask_output_for_tracking'], + use_multimask_token_for_obj_ptr=model_config['use_multimask_token_for_obj_ptr'], + compile_image_encoder=model_config['compile_image_encoder'], + multimask_min_pt_num=model_config['multimask_min_pt_num'], + multimask_max_pt_num=model_config['multimask_max_pt_num'], + use_mlp_for_obj_ptr_proj=model_config['use_mlp_for_obj_ptr_proj'], + binarize_mask_from_pts_for_mem_enc=True if segmentor == 'video' else False, + ).to(dtype).to(device).eval() + + # Load the state dictionary + sd = load_torch_file(model_path) + + # Initialize model based on segmentor type + if segmentor == 'single_image': + model_class = SAM2Base + model = initialize_model(model_class, model_config, segmentor, image_encoder, memory_attention, memory_encoder, sam_mask_decoder_extra_args, dtype, device) + model.load_state_dict(sd) + model = SAM2ImagePredictor(model) + elif segmentor == 'video': + model_class = SAM2VideoPredictor + model = initialize_model(model_class, model_config, segmentor, image_encoder, memory_attention, memory_encoder, sam_mask_decoder_extra_args, dtype, device) + model.load_state_dict(sd) + elif segmentor == 'automaskgenerator': + model_class = SAM2Base + model = initialize_model(model_class, model_config, segmentor, image_encoder, memory_attention, memory_encoder, sam_mask_decoder_extra_args, dtype, device) + model.load_state_dict(sd) + model = SAM2AutomaticMaskGenerator(model) + else: + raise ValueError(f"Segmentor {segmentor} not supported") + return model + +class LS_SAM2_ULTRA: + + model_path = os.path.join(folder_paths.models_dir, 'sam2') + model_dict = get_files(model_path, ['safetensors']) + def __init__(self): + self.NODE_NAME = 'SAM2 Ultra' + pass + + @classmethod + def INPUT_TYPES(cls): + sam2_model_list = list(cls.model_dict.keys()) + model_precision_list = [ 'fp16','bf16','fp32'] + select_list = ["all", "first", "by_index"] + method_list = ['VITMatte', 'VITMatte(local)', 'PyMatting', 'GuidedFilter', ] + device_list = ['cuda','cpu'] + return { + "required": { + "image": ("IMAGE",), + "bboxes": ("BBOXES",), + "sam2_model": (sam2_model_list,), + "precision": (model_precision_list,), + "bbox_select": (select_list,), + "select_index": ("STRING", {"default": "0,"},), + "cache_model": ("BOOLEAN", {"default": False}), + "detail_method": (method_list,), + "detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}), + "detail_dilate": ("INT", {"default": 4, "min": 1, "max": 255, "step": 1}), + "black_point": ("FLOAT", {"default": 0.15, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}), + "white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}), + "process_detail": ("BOOLEAN", {"default": True}), + "device": (device_list,), + "max_megapixels": ("FLOAT", {"default": 2.0, "min": 1, "max": 999, "step": 0.1}), + }, + "optional": { + } + } + + RETURN_TYPES = ("IMAGE", "MASK",) + RETURN_NAMES = ("image", "mask",) + FUNCTION = 'sam2_ultra' + CATEGORY = '😺dzNodes/LayerMask' + + def sam2_ultra(self, image, bboxes, sam2_model, precision, + bbox_select, select_index, cache_model, + detail_method, detail_erode, detail_dilate, black_point, white_point, + process_detail, device, max_megapixels, + ): + + ret_images = [] + ret_masks = [] + + # load model + sam2_path = os.path.join(folder_paths.models_dir, "sam2") + model_path = os.path.join(sam2_path, sam2_model) + if device == "cuda": + if torch.cuda.get_device_properties(0).major >= 8: + # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + sam2_device = {"cuda": torch.device("cuda"), "cpu": torch.device("cpu")}[device] + segmentor = 'single_image' + model_mapping = { + "base": "sam2_hiera_b+.yaml", + "large": "sam2_hiera_l.yaml", + "small": "sam2_hiera_s.yaml", + "tiny": "sam2_hiera_t.yaml" + } + + model_cfg_path = next( + (os.path.join(os.path.dirname(os.path.abspath(__file__)), "sam2", "sam2_configs", cfg) for key, cfg in model_mapping.items() if key in sam2_model), + None + ) + + model = load_model(model_path, model_cfg_path, segmentor, dtype, sam2_device) + + offload_device = mm.unet_offload_device() + + B, H, W, C = image.shape + indexs = extract_numbers(select_index) + + # Handle possible bboxes + if len(bboxes) == 0: + log(f"{self.NODE_NAME} skipped, because bboxes is empty.", message_type='error') + return (image, None) + else: + boxes_np_batch = [] + for bbox_list in bboxes: + boxes_np = [] + for bbox in bbox_list: + boxes_np.append(bbox) + boxes_np = np.array(boxes_np) + boxes_np_batch.append(boxes_np) + if bbox_select == "all": + final_box = np.array(boxes_np_batch) + elif bbox_select == "by_index": + final_box = [] + try: + for i in indexs: + final_box.append(boxes_np_batch[i]) + except IndexError: + log(f"{self.NODE_NAME} invalid bbox index {i}", message_type='warning') + else: + final_box = np.array(boxes_np_batch[0]) + final_labels = None + + mask_list = [] + try: + model.to(sam2_device) + except: + model.model.to(sam2_device) + + autocast_condition = not mm.is_device_mps(sam2_device) + with torch.autocast(mm.get_autocast_device(sam2_device), dtype=dtype) if autocast_condition else nullcontext(): + + image_np = (image.contiguous() * 255).byte().numpy() + comfy_pbar = ProgressBar(len(image_np)) + tqdm_pbar = tqdm(total=len(image_np), desc="Processing Images") + for i in range(len(image_np)): + model.set_image(image_np[i]) + if bboxes is None: + input_box = None + else: + if len(image_np) > 1: + input_box = final_box[i] + input_box = final_box + + out_masks, scores, logits = model.predict( + point_coords=None, + point_labels=None, + box=input_box, + multimask_output=True, + mask_input=None, + ) + + if out_masks.ndim == 3: + sorted_ind = np.argsort(scores)[::-1] + out_masks = out_masks[sorted_ind][0] # choose only the best result for now + scores = scores[sorted_ind] + logits = logits[sorted_ind] + mask_list.append(np.expand_dims(out_masks, axis=0)) + else: + _, _, H, W = out_masks.shape + # Combine masks for all object IDs in the frame + combined_mask = np.zeros((H, W), dtype=bool) + for out_mask in out_masks: + combined_mask = np.logical_or(combined_mask, out_mask) + combined_mask = combined_mask.astype(np.uint8) + mask_list.append(combined_mask) + comfy_pbar.update(1) + tqdm_pbar.update(1) + + if cache_model: + try: + model.to(offload_device) + except: + model.model.to(offload_device) + else: + del model + clear_memory() + + out_list = [] + for mask in mask_list: + mask_tensor = torch.from_numpy(mask) + mask_tensor = mask_tensor.permute(1, 2, 0) + mask_tensor = mask_tensor[:, :, 0] + out_list.append(mask_tensor) + mask_tensor = torch.stack(out_list, dim=0).cpu().float() + _mask = mask_tensor.squeeze() + + if detail_method == 'VITMatte(local)': + local_files_only = True + else: + local_files_only = False + orig_image = tensor2pil(image[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: + _trimap = generate_VITMatte_trimap(_mask, detail_erode, detail_dilate) + _mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device, + max_megapixels=max_megapixels) + _mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point)) + else: + _mask = mask2image(_mask) + + ret_image = RGB2RGBA(orig_image, _mask.convert('L')) + ret_images.append(pil2tensor(ret_image)) + ret_masks.append(image2mask(_mask)) + + log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish') + return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0)) + + +class LS_SAM2_VIDEO_ULTRA: + + model_path = os.path.join(folder_paths.models_dir, 'sam2') + model_dict = get_files(model_path, ['safetensors']) + def __init__(self): + self.NODE_NAME = 'SAM2 Video Ultra' + pass + + @classmethod + def INPUT_TYPES(cls): + sam2_model_list = list(cls.model_dict.keys()) + model_precision_list = ['fp16','bf16'] + method_list = ['VITMatte'] + device_list = ['cuda'] + return { + "required": { + "image": ("IMAGE",), + "bboxes": ("BBOXES",), + "sam2_model": (sam2_model_list,), + "precision": (model_precision_list,), + "cache_model": ("BOOLEAN", {"default": False}), + "individual_objects": ("BOOLEAN", {"default": False}), + "mask_preview_color": ("STRING", {"default": "#FF0080"},), + "detail_method": (method_list,), + "detail_erode": ("INT", {"default": 6, "min": 1, "max": 255, "step": 1}), + "detail_dilate": ("INT", {"default": 4, "min": 1, "max": 255, "step": 1}), + "black_point": ("FLOAT", {"default": 0.15, "min": 0.01, "max": 0.98, "step": 0.01, "display": "slider"}), + "white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01, "display": "slider"}), + "process_detail": ("BOOLEAN", {"default": True}), + "device": (device_list,), + "max_megapixels": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 10, "step": 0.1}), + }, + "optional": { + "pre_mask": ("MASK",), + } + } + + RETURN_TYPES = ("MASK","IMAGE") + RETURN_NAMES = ("mask","preview") + FUNCTION = 'sam2_video_ultra' + CATEGORY = '😺dzNodes/LayerMask' + + def sam2_video_ultra(self, image, bboxes, sam2_model, precision, + cache_model, individual_objects, mask_preview_color, + detail_method, detail_erode, detail_dilate, black_point, white_point, + process_detail, device, max_megapixels, + pre_mask=None + ): + + # load model + sam2_path = os.path.join(folder_paths.models_dir, "sam2") + model_path = os.path.join(sam2_path, sam2_model) + if device == "cuda": + if torch.cuda.get_device_properties(0).major >= 8: + # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices) + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision] + sam2_device = {"cuda": torch.device("cuda"), "cpu": torch.device("cpu")}[device] + segmentor = 'video' + model_mapping = { + "base": "sam2_hiera_b+.yaml", + "large": "sam2_hiera_l.yaml", + "small": "sam2_hiera_s.yaml", + "tiny": "sam2_hiera_t.yaml" + } + + model_cfg_path = next( + (os.path.join(os.path.dirname(os.path.abspath(__file__)), "sam2", "sam2_configs", cfg) for key, cfg in model_mapping.items() if key in sam2_model), + None + ) + + model = load_model(model_path, model_cfg_path, segmentor, dtype, sam2_device) + + offload_device = mm.unet_offload_device() + + B, H, W, C = image.shape + + if pre_mask is not None: + input_mask = pre_mask.clone().unsqueeze(1) + input_mask = F.interpolate(input_mask, size=(256, 256), mode="bilinear") + input_mask = input_mask.squeeze(1) + + + model_input_image_size = model.image_size + from comfy.utils import common_upscale + resized_image = common_upscale(image.movedim(-1,1), model_input_image_size, model_input_image_size, "bilinear", "disabled").movedim(1,-1) + + # Handle possible bboxes + if len(bboxes) == 0: + log(f"{self.NODE_NAME} skipped, because bboxes is empty.", message_type='error') + return (image, None) + else: + coords = bboxes2coordinates(bboxes) + + if not individual_objects: + positive_point_coords = np.atleast_2d(np.array(coords)) + else: + positive_point_coords = np.array([np.atleast_2d(coord) for coord in coords]) + + if not individual_objects: + positive_point_labels = np.ones(len(positive_point_coords)) + else: + positive_labels = [] + for point in positive_point_coords: + positive_labels.append(np.array([1])) # 1) + positive_point_labels = np.stack(positive_labels, axis=0) + + final_coords = positive_point_coords + final_labels = positive_point_labels + try: + model.to(sam2_device) + except: + model.model.to(sam2_device) + + autocast_condition = not mm.is_device_mps(sam2_device) + with torch.autocast(mm.get_autocast_device(sam2_device), dtype=dtype) if autocast_condition else nullcontext(): + mask_list = [] + if hasattr(self, 'inference_state'): + model.reset_state(self.inference_state) + self.inference_state = model.init_state(resized_image.permute(0, 3, 1, 2).contiguous(), H, W, device=sam2_device) + + if individual_objects: + for i, (coord, label) in enumerate(zip(final_coords, final_labels)): + _, out_obj_ids, out_mask_logits = model.add_new_points( + inference_state=self.inference_state, + frame_idx=0, + obj_id=i, + points=final_coords[i], + labels=final_labels[i], + ) + else: + _, out_obj_ids, out_mask_logits = model.add_new_points( + inference_state=self.inference_state, + frame_idx=0, + obj_id=1, + points=final_coords, + labels=final_labels, + ) + + pbar = ProgressBar(B) + video_segments = {} + for out_frame_idx, out_obj_ids, out_mask_logits in model.propagate_in_video(self.inference_state): + video_segments[out_frame_idx] = { + out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy() + for i, out_obj_id in enumerate(out_obj_ids) + } + pbar.update(1) + if individual_objects: + _, _, H, W = out_mask_logits.shape + # Combine masks for all object IDs in the frame + combined_mask = np.zeros((H, W), dtype=np.uint8) + for i, out_obj_id in enumerate(out_obj_ids): + out_mask = (out_mask_logits[i] > 0.0).cpu().numpy() + combined_mask = np.logical_or(combined_mask, out_mask) + video_segments[out_frame_idx] = combined_mask + + if individual_objects: + for frame_idx, combined_mask in video_segments.items(): + mask_list.append(combined_mask) + else: + for frame_idx, obj_masks in video_segments.items(): + for out_obj_id, out_mask in obj_masks.items(): + mask_list.append(out_mask) + + if cache_model: + try: + model.to(offload_device) + except: + model.model.to(offload_device) + else: + del model + clear_memory() + + out_list = [] + for mask in mask_list: + mask_tensor = torch.from_numpy(mask) + mask_tensor = mask_tensor.permute(1, 2, 0) + mask_tensor = mask_tensor[:, :, 0] + out_list.append(mask_tensor) + out_list = torch.stack(out_list, dim=0).cpu().float() + + if detail_method == 'VITMatte(local)': + local_files_only = True + else: + local_files_only = False + + detail_range = detail_erode + detail_dilate + ret_previews = [] + ret_masks = [] + from tqdm import tqdm + comfy_pbar = ProgressBar(len(image)) + tqdm_pbar = tqdm(total=len(image), desc="Processing VitMatte") + for index, img in tqdm(enumerate(image)): + orig_image = tensor2pil(img) + _mask = out_list[index].unsqueeze(0) + _mask = mask2image(_mask).resize((orig_image.size), Image.BILINEAR) + if process_detail: + _trimap = generate_VITMatte_trimap(pil2tensor(_mask), detail_erode, detail_dilate) + _mask = generate_VITMatte(orig_image, _trimap, local_files_only=local_files_only, device=device, + max_megapixels=max_megapixels) + _mask = tensor2pil(histogram_remap(pil2tensor(_mask), black_point, white_point)) + + color_image = Image.new("RGB", orig_image.size,color=mask_preview_color) + color_image = chop_image_v2(orig_image, color_image, "normal", 50) + color_image.paste(orig_image, mask=_mask) + ret_previews.append(pil2tensor(color_image)) + ret_masks.append(image2mask(_mask)) + comfy_pbar.update(1) + tqdm_pbar.update(1) + + log(f"{self.NODE_NAME} Processed {len(ret_masks)} frame(s).", message_type='finish') + return (torch.cat(ret_masks, dim=0), torch.cat(ret_previews, dim=0)) + + +NODE_CLASS_MAPPINGS = { + "LayerMask: SAM2Ultra": LS_SAM2_ULTRA, + "LayerMask: SAM2VideoUltra": LS_SAM2_VIDEO_ULTRA +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "LayerMask: SAM2Ultra": "LayerMask: SAM2 Ultra", + "LayerMask: SAM2VideoUltra": "LayerMask: SAM2 Video Ultra" +} \ No newline at end of file diff --git a/py/yolov8_detect.py b/py/yolov8_detect.py index 5812512..0b119d1 100644 --- a/py/yolov8_detect.py +++ b/py/yolov8_detect.py @@ -1,28 +1,21 @@ import copy import os.path - from .imagefunc import * -NODE_NAME = 'YoloV8Detect' - model_path = os.path.join(folder_paths.models_dir, 'yolo') class YoloV8Detect: def __init__(self): - pass + self.NODE_NAME = 'YoloV8Detect' + @classmethod def INPUT_TYPES(self): - __file_list = glob.glob(model_path + '/*.pt') - # __file_list.extend(glob.glob(model_path + '/*.safetensors')) - FILES_DICT = {} - for i in range(len(__file_list)): - _, __filename = os.path.split(__file_list[i]) - FILES_DICT[__filename] = __file_list[i] + model_ext = [".pt"] + FILES_DICT = get_files(model_path, model_ext) FILE_LIST = list(FILES_DICT.keys()) - mask_merge = ["all", "1", "2", "3", "4", "5", "6", "7", "8", "9"] return { "required": { @@ -90,7 +83,7 @@ class YoloV8Detect: _mask = add_mask(_mask, ret_yolo_masks[i + 1]) ret_masks.append(_mask) - log(f"{NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish') + log(f"{self.NODE_NAME} Processed {len(ret_masks)} image(s).", message_type='finish') return (torch.cat(ret_masks, dim=0), torch.cat(ret_yolo_plot_images, dim=0), torch.cat(ret_yolo_masks, dim=0),) diff --git a/pyproject.toml b/pyproject.toml index 5c68c2a..19a23d8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,9 +1,9 @@ [project] name = "comfyui_layerstyle" description = "A set of nodes for ComfyUI it generate image like Adobe Photoshop's Layer Style. the Drop Shadow is first completed node, and follow-up work is in progress." -version = "1.0.38" +version = "1.0.39" license = "MIT" -dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "transparent-background", "huggingface_hub", "accelerate", "bitsandbytes", "torchscale", "wandb", "hydra-core", "psd-tools"] +dependencies = ["numpy", "pillow", "torch", "matplotlib", "Scipy", "scikit_image", "opencv-contrib-python", "pymatting", "segment_anything", "timm", "addict", "yapf", "colour-science", "wget", "mediapipe", "loguru", "typer_config", "fastapi", "rich", "google-generativeai", "diffusers", "omegaconf", "tqdm", "transformers", "kornia", "image-reward", "ultralytics", "blend_modes", "blind-watermark", "qrcode", "pyzbar", "transparent-background", "huggingface_hub", "accelerate", "bitsandbytes", "torchscale", "wandb", "hydra-core", "psd-tools", "inference-cli[yolo-world]", "inference-gpu[yolo-world]", "onnxruntime"] [project.urls] Repository = "https://github.com/chflame163/ComfyUI_LayerStyle" diff --git a/requirements.txt b/requirements.txt index 96cc081..1819624 100644 --- a/requirements.txt +++ b/requirements.txt @@ -35,4 +35,7 @@ bitsandbytes>=0.41.1 torchscale wandb psd-tools -hydra-core \ No newline at end of file +hydra-core +inference-cli[yolo-world]>=0.13.0 +inference-gpu[yolo-world]>=0.13.0 +onnxruntime \ No newline at end of file diff --git a/workflow/1280x720_seven_person.jpg b/workflow/1280x720_seven_person.jpg new file mode 100644 index 0000000..29ac5f6 Binary files /dev/null and b/workflow/1280x720_seven_person.jpg differ diff --git a/workflow/sam2_ultra_example.json b/workflow/sam2_ultra_example.json new file mode 100644 index 0000000..94fed04 --- /dev/null +++ b/workflow/sam2_ultra_example.json @@ -0,0 +1,888 @@ +{ + "last_node_id": 25, + "last_link_id": 37, + "nodes": [ + { + "id": 2, + "type": "LoadImage", + "pos": { + "0": 60, + "1": 180, + "2": 0, + "3": 0, + "4": 0, + "5": 0, + "6": 0, + "7": 0, + "8": 0, + "9": 0 + }, + "size": { + "0": 315, + "1": 314 + }, + "flags": {}, + "order": 0, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "IMAGE", + "type": "IMAGE", + "links": [ + 1, + 6, + 10, + 18, + 22, + 25 + ], + "slot_index": 0, + "shape": 3 + }, + { + "name": "MASK", + "type": "MASK", + "links": null, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "LoadImage" + }, + "widgets_values": [ + "seven_persons_1280x720.jpg", + "image" + ] + }, + { + "id": 6, + "type": "LayerMask: ObjectDetectorYOLOWorld", + "pos": { + "0": 491, + "1": 540, + "2": 0, + "3": 0, + "4": 0, + "5": 0, + "6": 0, + "7": 0, + "8": 0, + "9": 0 + }, + "size": { + "0": 310.79998779296875, + "1": 206.62655639648438 + }, + "flags": {}, + "order": 3, + "mode": 0, + "inputs": [ + { + "name": "image", + "type": "IMAGE", + "link": 6 + } + ], + "outputs": [ + { + "name": "bboxes", + "type": "BBOXES", + "links": [ + 36 + ], + "slot_index": 0, + "shape": 3 + }, + { + "name": "preview", + "type": "IMAGE", + "links": null, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "LayerMask: ObjectDetectorYOLOWorld" + }, + "widgets_values": [ + "yolo_world/v2-x", + 0.05, + 0.3, + "person", + "by_index", + "0,3,4" + ] + }, + { + "id": 4, + "type": "LayerMask: LoadFlorence2Model", + "pos": { + "0": 502, + "1": 132, + "2": 0, + "3": 0, + "4": 0, + "5": 0, + "6": 0, + "7": 0, + "8": 0, + "9": 0 + }, + "size": { + "0": 301.9246520996094, + "1": 65.07791900634766 + }, + "flags": {}, + "order": 1, + "mode": 0, + "inputs": [], + "outputs": [ + { + "name": "florence2_model", + "type": "FLORENCE2", + "links": [ + 2 + ], + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "LayerMask: LoadFlorence2Model" + }, + "widgets_values": [ + "base" + ] + }, + { + "id": 3, + "type": "LayerMask: ObjectDetectorFL2", + "pos": { + "0": 502, + "1": 247, + "2": 0, + "3": 0, + "4": 0, + "5": 0, + "6": 0, + "7": 0, + "8": 0, + "9": 0 + }, + "size": { + 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+ "links": [ + 37 + ], + "slot_index": 0, + "shape": 3 + }, + { + "name": "preview", + "type": "IMAGE", + "links": null, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "LayerMask: ObjectDetectorMask" + }, + "widgets_values": [ + "all", + "0," + ] + }, + { + "id": 25, + "type": "LayerMask: BBoxJoin", + "pos": { + "0": 858, + "1": 687, + "2": 0, + "3": 0, + "4": 0, + "5": 0, + "6": 0, + "7": 0, + "8": 0, + "9": 0 + }, + "size": { + "0": 252, + "1": 86 + }, + "flags": {}, + "order": 9, + "mode": 0, + "inputs": [ + { + "name": "bboxes_1", + "type": "BBOXES", + "link": 36 + }, + { + "name": "bboxes_2", + "type": "BBOXES", + "link": 37 + }, + { + "name": "bboxes_3", + "type": "BBOXES", + "link": null + }, + { + "name": "bboxes_4", + "type": "BBOXES", + "link": null + } + ], + "outputs": [ + { + "name": "bboxes", + "type": "BBOXES", + "links": [ + 33 + ], + "shape": 3, + "slot_index": 0 + } + ], + "properties": { + "Node name for S&R": "LayerMask: BBoxJoin" + } + }, + { + "id": 22, + "type": "LayerMask: SAM2Ultra", + "pos": { + "0": 1170, + "1": 550, + "2": 0, + "3": 0, + "4": 0, + "5": 0, + "6": 0, + "7": 0, + "8": 0, + "9": 0 + }, + "size": { + "0": 315, + "1": 366 + }, + "flags": {}, + "order": 12, + "mode": 0, + "inputs": [ + { + "name": "image", + "type": "IMAGE", + "link": 22 + }, + { + "name": "bboxes", + "type": "BBOXES", + "link": 33 + } + ], + "outputs": [ + { + "name": "image", + "type": "IMAGE", + "links": [ + 24 + ], + "slot_index": 0, + "shape": 3 + }, + { + "name": "mask", + "type": "MASK", + "links": null, + "shape": 3 + } + ], + "properties": { + "Node name for S&R": "LayerMask: SAM2Ultra" + }, + "widgets_values": [ + "sam2_hiera_base_plus.safetensors", + "fp16", + "all", + "0,", + false, + "VITMatte", + 6, + 4, + 0.15, + 0.99, + true, + "cuda", + 2 + ] + }, + { + "id": 23, + "type": "LayerMask: SAM2Ultra", + "pos": { + "0": 1170, + "1": 980, + "2": 0, + "3": 0, + "4": 0, + "5": 0, + "6": 0, + "7": 0, + "8": 0, + "9": 0 + }, + "size": { 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