From 27849b7a76c666bb97027ad446f5d217cac478e7 Mon Sep 17 00:00:00 2001 From: Tingquan Gao Date: Tue, 22 Jun 2021 11:08:55 +0800 Subject: [PATCH] Update en doc of Hubserving (#900) --- deploy/hubserving/readme_en.md | 63 +++++--- deploy/hubserving/test_hubserving.py | 12 +- .../backbone/model_zoo/vision_transformer.py | 142 ++++++++++++++---- 3 files changed, 160 insertions(+), 57 deletions(-) diff --git a/deploy/hubserving/readme_en.md b/deploy/hubserving/readme_en.md index 5863ce97..d7ad8840 100644 --- a/deploy/hubserving/readme_en.md +++ b/deploy/hubserving/readme_en.md @@ -4,7 +4,7 @@ English | [简体中文](readme.md) HubServing service pack contains 3 files, the directory is as follows: ``` -deploy/hubserving/clas/ +hubserving/clas/ └─ __init__.py Empty file, required └─ config.json Configuration file, optional, passed in as a parameter when using configuration to start the service └─ module.py Main module file, required, contains the complete logic of the service @@ -22,22 +22,28 @@ pip3 install paddlehub==2.0.0b1 --upgrade -i https://pypi.tuna.tsinghua.edu.cn/s Before installing the service module, you need to prepare the inference model and put it in the correct path. The default model path is: ``` -Model structure file: ./inference/cls_infer.pdmodel -Model parameters file: ./inference/cls_infer.pdiparams +Model structure file: PaddleClas/inference/inference.pdmodel +Model parameters file: PaddleClas/inference/inference.pdiparams ``` -**The model path can be found and modified in `params.py`.** More models provided by PaddleClas can be obtained from the [model library](../../docs/en/models/models_intro_en.md). You can also use models trained by yourself. +* The model file path can be viewed and modified in `PaddleClas/deploy/hubserving/clas/params.py`. + + It should be noted that the prefix of model structure file and model parameters file must be `inference`. + +* More models provided by PaddleClas can be obtained from the [model library](../../docs/en/models/models_intro_en.md). You can also use models trained by yourself. ### 3. Install Service Module * On Linux platform, the examples are as follows. ```shell -hub install deploy/hubserving/clas/ +cd PaddleClas/deploy +hub install hubserving/clas/ ``` * On Windows platform, the examples are as follows. ```shell -hub install deploy\hubserving\clas\ +cd PaddleClas\deploy +hub install hubserving\clas\ ``` ### 4. Start service @@ -103,32 +109,30 @@ Wherein, the format of `config.json` is as follows: For example, use GPU card No. 3 to start the 2-stage series service: ```shell +cd PaddleClas/deploy export CUDA_VISIBLE_DEVICES=3 -hub serving start -c deploy/hubserving/clas/config.json +hub serving start -c hubserving/clas/config.json ``` ## Send prediction requests After the service starts, you can use the following command to send a prediction request to obtain the prediction result: ```shell -python tools/test_hubserving.py server_url image_path -``` +cd PaddleClas/deploy +python hubserving/test_hubserving.py server_url image_path +``` Two required parameters need to be passed to the script: - **server_url**: service address，format of which is `http://[ip_address]:[port]/predict/[module_name]` - **image_path**: Test image path, can be a single image path or an image directory path -- **top_k**: [**Optional**] Return the top `top_k` 's scores ,default by `1`. - **batch_size**: [**Optional**] batch_size. Default by `1`. -- **resize_short**: [**Optional**] Resize the input image according to short size. Default by `256`. -- **resize**: [**Optional**] Resize the input image. Default by `224`. -- **normalize**: [**Optional**] Whether normalize the input image. Default by `True`. **Notice**: If you want to use `Transformer series models`, such as `DeiT_***_384`, `ViT_***_384`, etc., please pay attention to the input size of model, and need to set `--resize_short=384`, `--resize=384`. **Eg.** ```shell -python tools/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./deploy/hubserving/ILSVRC2012_val_00006666.JPEG --top_k 5 +python hubserving/test_hubserving.py --server_url http://127.0.0.1:8866/predict/clas_system --image_file ./hubserving/ILSVRC2012_val_00006666.JPEG --batch_size 8 ``` ### Returned result format @@ -142,7 +146,7 @@ list: The returned results └─ float: The time cost of predicting the picture, unit second ``` -**Note：** If you need to add, delete or modify the returned fields, you can modify the file `module.py` of the corresponding module. For the complete process, refer to the user-defined modification service module in the next section. +**Note：** If you need to add, delete or modify the returned fields, you can modify the corresponding module. For the details, refer to the user-defined modification service module in the next section. ## User defined service module modification If you need to modify the service logic, the following steps are generally required: @@ -151,20 +155,41 @@ If you need to modify the service logic, the following steps are generally requi ```shell hub serving stop --port/-p XXXX ``` -2. Modify the code in the corresponding files, like `module.py` and `params.py`, according to the actual needs. -For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `cfg.model_file` and `cfg.params_file` in `params.py`. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. - After modifying and installing (`hub install deploy/hubserving/clas/`) and before deploying, you can use `python deploy/hubserving/clas/test.py` to test the installed service module. +2. Modify the code in the corresponding files, like `module.py` and `params.py`, according to the actual needs. You need re-install(hub install hubserving/clas/) and re-deploy after modifing `module.py`. +After modifying and installing and before deploying, you can use `python hubserving/clas/module.py` to test the installed service module. + +For example, if you need to replace the model used by the deployed service, you need to modify model path parameters `cfg.model_file` and `cfg.params_file` in `params.py`. Of course, other related parameters may need to be modified at the same time. Please modify and debug according to the actual situation. 3. Uninstall old service module ```shell hub uninstall clas_system ``` + 4. Install modified service module ```shell -hub install deploy/hubserving/clas/ +hub install hubserving/clas/ ``` + 5. Restart service ```shell hub serving start -m clas_system ``` + +**Note**: + +Common parameters can be modified in params.py: +* Directory of model files(include model structure file and model parameters file): + ```python + "inference_model_dir": + ``` +* The number of Top-k results returned during post-processing: + ```python + 'topk': + ``` +* Mapping file corresponding to label and class ID during post-processing: + ```python + 'class_id_map_file': + ``` + +In order to avoid unnecessary delay and be able to predict in batch, the preprocessing (include resize, crop and other) is completed in the client, so modify [test_hubserving.py](./test_hubserving.py#L35-L52) if necessary. diff --git a/deploy/hubserving/test_hubserving.py b/deploy/hubserving/test_hubserving.py index 00f3db0e..c738cc53 100644 --- a/deploy/hubserving/test_hubserving.py +++ b/deploy/hubserving/test_hubserving.py @@ -69,8 +69,8 @@ def main(args): img = cv2.imread(img_path) if img is None: logger.warning( - "Image file failed to read and has been skipped. The path: {}". - format(img_path)) + f"Image file failed to read and has been skipped. The path: {img_path}" + ) continue else: for ops in preprocess_ops: @@ -101,8 +101,7 @@ def main(args): msg = r.json()["msg"] raise Exception(msg) except Exception as e: - logger.error("{}, in file(s): {} etc.".format(e, img_name_list[ - 0])) + logger.error(f"{e}, in file(s): {img_name_list[0]} etc.") continue else: results = r.json()["results"] @@ -120,8 +119,9 @@ def main(args): result_list["class_ids"][i], result_list["scores"][i]) - logger.info("File:{}, The result(s): {}".format( - img_name_list[number], result_str)) + logger.info( + f"File:{img_name_list[number]}, The result(s): {result_str}" + ) finally: img_data_list = [] diff --git a/ppcls/arch/backbone/model_zoo/vision_transformer.py b/ppcls/arch/backbone/model_zoo/vision_transformer.py index 4bb39890..ebe77684 100644 --- a/ppcls/arch/backbone/model_zoo/vision_transformer.py +++ b/ppcls/arch/backbone/model_zoo/vision_transformer.py @@ -22,20 +22,28 @@ from paddle.nn.initializer import TruncatedNormal, Constant, Normal from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url MODEL_URLS = { - "ViT_small_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams", - "ViT_base_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams", - "ViT_base_patch16_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams", - "ViT_base_patch32_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams", - "ViT_large_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams", - "ViT_large_patch16_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams", - "ViT_large_patch32_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams", - "ViT_huge_patch16_224": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_huge_patch16_224_pretrained.pdparams", - "ViT_huge_patch32_384": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_huge_patch32_384_pretrained.pdparams" - } + "ViT_small_patch16_224": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams", + "ViT_base_patch16_224": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams", + "ViT_base_patch16_384": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams", + "ViT_base_patch32_384": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams", + "ViT_large_patch16_224": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams", + "ViT_large_patch16_384": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams", + "ViT_large_patch32_384": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams", + "ViT_huge_patch16_224": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_huge_patch16_224_pretrained.pdparams", + "ViT_huge_patch32_384": + "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_huge_patch32_384_pretrained.pdparams" +} __all__ = list(MODEL_URLS.keys()) - trunc_normal_ = TruncatedNormal(std=.02) normal_ = Normal zeros_ = Constant(value=0.) @@ -209,7 +217,7 @@ class PatchEmbed(nn.Layer): def forward(self, x): B, C, H, W = x.shape assert H == self.img_size[0] and W == self.img_size[1], \ - "Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." x = self.proj(x).flatten(2).transpose((0, 2, 1)) return x @@ -323,8 +331,11 @@ def _load_pretrained(pretrained, model, model_url, use_ssld=False): ) - -def ViT_small_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_small_patch16_224(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, @@ -333,12 +344,19 @@ def ViT_small_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs mlp_ratio=3, qk_scale=768**-0.5, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_small_patch16_224"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_small_patch16_224"], + use_ssld=use_ssld) return model - -def ViT_base_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_base_patch16_224(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( patch_size=16, embed_dim=768, @@ -348,11 +366,19 @@ def ViT_base_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs) qkv_bias=True, epsilon=1e-6, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch16_224"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_base_patch16_224"], + use_ssld=use_ssld) return model -def ViT_base_patch16_384(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_base_patch16_384(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( img_size=384, patch_size=16, @@ -363,11 +389,19 @@ def ViT_base_patch16_384(pretrained, model, model_url, use_ssld=False, **kwargs) qkv_bias=True, epsilon=1e-6, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch16_384"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_base_patch16_384"], + use_ssld=use_ssld) return model -def ViT_base_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_base_patch32_384(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( img_size=384, patch_size=32, @@ -378,11 +412,19 @@ def ViT_base_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs) qkv_bias=True, epsilon=1e-6, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_base_patch32_384"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_base_patch32_384"], + use_ssld=use_ssld) return model -def ViT_large_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_large_patch16_224(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( patch_size=16, embed_dim=1024, @@ -392,11 +434,19 @@ def ViT_large_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs qkv_bias=True, epsilon=1e-6, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch16_224"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_large_patch16_224"], + use_ssld=use_ssld) return model -def ViT_large_patch16_384(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_large_patch16_384(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( img_size=384, patch_size=16, @@ -407,11 +457,19 @@ def ViT_large_patch16_384(pretrained, model, model_url, use_ssld=False, **kwargs qkv_bias=True, epsilon=1e-6, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch16_384"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_large_patch16_384"], + use_ssld=use_ssld) return model -def ViT_large_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_large_patch32_384(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( img_size=384, patch_size=32, @@ -422,11 +480,19 @@ def ViT_large_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs qkv_bias=True, epsilon=1e-6, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_large_patch32_384"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_large_patch32_384"], + use_ssld=use_ssld) return model -def ViT_huge_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_huge_patch16_224(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( patch_size=16, embed_dim=1280, @@ -434,11 +500,19 @@ def ViT_huge_patch16_224(pretrained, model, model_url, use_ssld=False, **kwargs) num_heads=16, mlp_ratio=4, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_huge_patch16_224"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_huge_patch16_224"], + use_ssld=use_ssld) return model -def ViT_huge_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs): +def ViT_huge_patch32_384(pretrained, + model, + model_url, + use_ssld=False, + **kwargs): model = VisionTransformer( img_size=384, patch_size=32, @@ -447,5 +521,9 @@ def ViT_huge_patch32_384(pretrained, model, model_url, use_ssld=False, **kwargs) num_heads=16, mlp_ratio=4, **kwargs) - _load_pretrained(pretrained, model, MODEL_URLS["ViT_huge_patch32_384"], use_ssld=use_ssld) + _load_pretrained( + pretrained, + model, + MODEL_URLS["ViT_huge_patch32_384"], + use_ssld=use_ssld) return model -- GitLab