From 27849b7a76c666bb97027ad446f5d217cac478e7 Mon Sep 17 00:00:00 2001
From: Tingquan Gao <gaotingquan@baidu.com>
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 5863ce97c..d7ad8840e 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 00f3db0e2..c738cc539 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 4bb39890f..ebe77684e 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