From 27127a5d7f50cea50f135948dcc6f600d6ab0ede Mon Sep 17 00:00:00 2001 From: shiyu22 Date: Thu, 9 Dec 2021 10:03:08 +0800 Subject: [PATCH 1/2] Update README Signed-off-by: shiyu22 --- README.md | 61 ++++++++++++++++++++++++++++++++++++++++++----- pic/pipeline.png | Bin 0 -> 272864 bytes 2 files changed, 55 insertions(+), 6 deletions(-) create mode 100644 pic/pipeline.png diff --git a/README.md b/README.md index 0d653b3..549034a 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,69 @@ # Image Embedding Pipeline with Resnet50 -Authors: name or github-name(email) +Authors: Kyle, shiyu22 ## Overview -Introduce the functions of pipeline. +This pipeline is used to **extract the feature vector of the image**, first to normalize the image , and then use resnet50 model to generate the vector. + +In fact, the pipeline runs by parsing [the yaml file](./image_embedding_resnet50.yaml), which declares some functions we call **Operator**, and the **DataFrame** required by each Operator. Next will introduce the interface, how to use it and how it works, have fun with it! ## Interface -The interface of pipeline.(input & output) +`towhee.pipeline(task: str, fmc: FileManagerConfig = FileManagerConfig(), branch: str = 'main', force_download: bool = False)` [source](https://github.com/towhee-io/towhee/blob/main/towhee/__init__.py) + +**param:** + +- **task**(str), task name or pipeline repo name. +- **fmc**(FileManagerConfig), optional file manager config for the local instance, default is FileManagerConfig(). +- **branch**(str), which branch to use for operators/pipelines on hub, defaults to 'main'. +- **force_download**(bool), whether to redownload pipeline and operators, default is False. + +**return:** + +- **_PipelineWrapper**, which is a wrapper class around `Pipeline`. + +When we declare a pipeline object with a specific task, such as `towhee/image-embedding-resnet50` in this repo, it will run according to the Yaml file, and the input and output are as follows: + +**inputs:** + +- **img_tensor**(PIL.Image), image to be embedded. + +**outputs:** + +- **cnn**(numpy.ndarray), the embedding of image. ## How to use -- Requirements from requirements.txt -- Run it with Towhee +1. Install [Towhee](https://github.com/towhee-io/towhee) + +```bash +$ pip3 install towhee +``` + +> You can refer to [Getting Started with Towhee](towhee.io) for more details. If you have questions, you can [submit an issue to the towhee repository](https://github.com/towhee-io/towhee/issues). + +2. Run it with Towhee + +```python +>>> from towhee import pipeline +>>> from PIL import Image + +>>> img = Image.open('./test_data/test.jpg') +>>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50') +>>> embedding = embedding_pipeline(img) +``` ## How it works -- op1->op2->op3 , and intro all the op used. (auto generate graph) \ No newline at end of file +First of all, you need to learn the pipeline and operator in Towhee architecture: + +- **Pipeline**: A `Pipeline` is a single machine learning task that is composed of several operators. Operators are connected together internally via a directed acyclic graph. + +- **Operator**: An `Operator` is a single node within a pipeline. It contains files (e.g. code, configs, models, etc...) and works for reusable operations (e.g., preprocessing an image, inference with a pretrained model). + +This pipeline includes four functions: `_start_op`, `towhee/transform-image`, `towhee/resnet50-image-embedding` and` _end_op`. It is necessary to ensure that the input and output of the four Operators correspond to each other, and the input and output data types can be defined by DataFrame. + +![img](./pic/pipeline.png) + +Among the four Operator,`_start_op` and `_end_op` are required in any Pipeline, and they are used to start and end the pipeline in the Towhee system. For the other two Operators, please refer to [towhee/transform-image](https://hub.towhee.io/towhee/transform-image) and [towhee/resnet50-image-embedding](https://hub.towhee.io/towhee/resnet50-image-embedding). diff --git a/pic/pipeline.png b/pic/pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..f8f33f4803af356119c10556f66323eab471b663 GIT binary patch literal 272864 zcmeEuWn5J2+P8#&N{J}aAfO`MT_RlqN(?2!(A}LX(mhCbm&8y*3ew#$l+rnL$Gc|7 z*`9sg_u1#;`{DV5KXhCy?sebS^{;CMzE+gNxl3~Q)~#DOGSaV9Zr#H2zI6-D6$=CS zo5X?J#%_9{{@Z3yubxY=z*c%t!t=ZcSgcB#td-uN2&NjR*s(E;qNq1&; zmb!*8ZeeFfC5w{ z{SEqa^{xk!CRkKHw^09!FS_*jpBC^+1+C>tP|)#3Z~fyJR&=cEg_r-u2azAC*YH`u zV-a?hC;!Lm{&q#GQ-AY+zJ=dDy89l*Po(~IiOuo9x@}S5MoRv>n-FadzKwOg`!l*@ z^!|VMd-{pgVEi|02V9Ni5!GpHuo8u;)PMJT0*g)Z-`&LjF7|({=>IPE-HiO9;4muAaQ&7-|87AD!R9g=06p2A34;3@JppnVPs4bCG!z}X9qG$ zsr^yB$}i)NvmYSqe{;YCs?){RDR%aM=myl13uA@qDjD(;uIGpOt_tfT*^#vzIkH*O z_r-hpHL-yIz7PuBl*^)~LH|EA77s(;+?#6#i6(k;=h0z0xz1)UPg;vfR!Ub%GF)U4 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ze$>9~-KoBtAO09$1fGreNXH`#vj_l+ba0%Nw_}5a0UK9Hc=+*-i^)mlN9R?Ad-PS@ z{C4Y`)&AMvCd(h2a4hF4U4{PCv2L~Td+n95)`Qqn2a?7(lN|Y5MN~!G3_o3KXU$^Mz8(;FGX{2 zsol3SY>O}Voeb<}vG|oYK9QJlJbZoUrQOf2Y68zcJNNO$qt=fXx8?eO7kcWCQm{ka zYAkT!&qbcRO+wI+3D)0Gc=_3m=l@y*V{0we$(tGhOM)L))D@K&nJl^A*cT`O&p4I? zPI~m7w!d$$cF#!9s{GZA&o#^t{b*5&=6r0K8s?}8LBJx)%i%Isb5KLQRRkEB9<>{Z xP^t+$pK}R|$y}_70ulsJsZpnn_)u-EXO{a|>=63ics&CUc)I$ztaD0e0sxJesKx*Q literal 0 HcmV?d00001 From d1969398382b4732ab3a38d10b11524122c386ba Mon Sep 17 00:00:00 2001 From: Kaiyuan Hu Date: Thu, 9 Dec 2021 12:13:22 +0800 Subject: [PATCH 2/2] Update README --- README.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 549034a..01f8d5f 100644 --- a/README.md +++ b/README.md @@ -4,34 +4,34 @@ Authors: Kyle, shiyu22 ## Overview -This pipeline is used to **extract the feature vector of the image**, first to normalize the image , and then use resnet50 model to generate the vector. +This pipeline is used to **extract the feature vector of the image**. First step is to normalize the image, and then use resnet50 model to generate the vector. -In fact, the pipeline runs by parsing [the yaml file](./image_embedding_resnet50.yaml), which declares some functions we call **Operator**, and the **DataFrame** required by each Operator. Next will introduce the interface, how to use it and how it works, have fun with it! +The pipeline parses [the yaml file](./image_embedding_resnet50.yaml), which declares some components we call **Operator** and **DataFrame**. Next, we will introduce the interface, show how to use it and how it works, have fun with it! ## Interface -`towhee.pipeline(task: str, fmc: FileManagerConfig = FileManagerConfig(), branch: str = 'main', force_download: bool = False)` [source](https://github.com/towhee-io/towhee/blob/main/towhee/__init__.py) +`towhee.pipeline(task: str, fmc: FileManagerConfig = FileManagerConfig(), branch: str = 'main', force_download: bool = False)` [\[source\]](https://github.com/towhee-io/towhee/blob/main/towhee/__init__.py) -**param:** +**params:** -- **task**(str), task name or pipeline repo name. -- **fmc**(FileManagerConfig), optional file manager config for the local instance, default is FileManagerConfig(). -- **branch**(str), which branch to use for operators/pipelines on hub, defaults to 'main'. -- **force_download**(bool), whether to redownload pipeline and operators, default is False. +- **task**(str): task name or pipeline repo name. +- **fmc**(FileManagerConfig): optional, file manager config for the local instance, default is a default FileManagerConfig obejct. +- **branch**(str): optional, which branch to use for operators/pipelines on hub, defaults to 'main'. +- **force_download**(bool): optional, whether to redownload pipeline and operators, default is False. **return:** -- **_PipelineWrapper**, which is a wrapper class around `Pipeline`. +- **_PipelineWrapper**, an instance of the wrapper class around `Pipeline`. -When we declare a pipeline object with a specific task, such as `towhee/image-embedding-resnet50` in this repo, it will run according to the Yaml file, and the input and output are as follows: +When we declare a pipeline object with a specific task, such as `towhee/image-embedding-resnet50` in this repo, it will run according to the Yaml file, and the input and output are: **inputs:** -- **img_tensor**(PIL.Image), image to be embedded. +- **img_tensor**(PIL.Image), the image to be encoded. **outputs:** -- **cnn**(numpy.ndarray), the embedding of image. +- **cnn**(numpy.ndarray), the embedding of the image. ## How to use @@ -41,7 +41,7 @@ When we declare a pipeline object with a specific task, such as `towhee/image-em $ pip3 install towhee ``` -> You can refer to [Getting Started with Towhee](towhee.io) for more details. If you have questions, you can [submit an issue to the towhee repository](https://github.com/towhee-io/towhee/issues). +> You can refer to [Getting Started with Towhee](towhee.io) for more details. If you have any questions, you can [submit an issue to the towhee repository](https://github.com/towhee-io/towhee/issues). 2. Run it with Towhee @@ -49,7 +49,7 @@ $ pip3 install towhee >>> from towhee import pipeline >>> from PIL import Image ->>> img = Image.open('./test_data/test.jpg') +>>> img = Image.open('path/to/your/image') >>> embedding_pipeline = pipeline('towhee/image-embedding-resnet50') >>> embedding = embedding_pipeline(img) ```