feat: add the doc and demo about gallery2fc
add description doc and demo configpull/1714/head
gaotingquan
Tingquan Gao
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本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleClas PP-ShiTu模型的详细步骤。
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Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。
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Paddle Lite是飞桨轻量化推理引擎，为手机、IoT端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。
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## 1. 准备环境
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@ -216,3 +216,6 @@ A1：如果已经走通了上述步骤，更换模型只需要替换 `.nb` 模
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Q2：换一个图测试怎么做？
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A2：替换 deploy 下的测试图像为你想要测试的图像，并重新生成json配置文件（或者直接修改图像路径），使用 ADB 再次 push 到手机上即可。
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Q3：如果需要更换模型/预训练模型/底库数据集，需要怎么做：
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A：请参考文档 [基于分类方法的 PP-Shitu 移动端部署方案说明](./gallery2fc.md)。
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# 识别模型转分类模型
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PaddleClas 提供了 `gallery2fc.py` 工具，帮助大家将识别模型转为分类模型。
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## 一、模型转换说明
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### 1.1 准备底库数据、预训练模型
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#### 1. 底库数据集
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首先需要准备好底库数据，下面以 PaddleClas 提供饮料数据集（drink_dataset_v1.0）为例进行说明，饮料数据集获取方法：
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```shell
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cd PaddleClas/
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/data/drink_dataset_v1.0.tar
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tar -xf drink_dataset_v1.0.tar
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```
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饮料数据集的底库图片路径为 `drink_dataset_v1.0/gallery/`，底库图片列表可在 `drink_dataset_v1.0/gallery/drink_label.txt` 中查看，关于底库数据格式说明，请参考文档[数据集格式说明](../data_preparation/recognition_dataset.md#1-数据集格式说明)。
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#### 2. 预训练模型
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在开始转换模型前，需要准备好预训练模型，下面以量化后的 `general_PPLCNet_x2_5` 模型为例，下载预训练模型：
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```shell
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cd PaddleClas/pretrained/
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wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/general_PPLCNet_x2_5_pretrained_v1.0_quant.pdparams
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```
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### 1.2 准备配置文件
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在进行模型转换时，需要通过配置文件定义所需参数，本例中所用配置文件为 `ppcls/configs/GeneralRecognition/Gallery2FC_PPLCNet_x2_5.yaml`，对于配置文件字段的说明，如下所示：
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* Global:
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* pretrained_model: 预训练模型路径，无需包含 `.pdparams` 后缀名；
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* image_shape: 模型输入数据尺寸，无需包含 batch size 维度；
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* save_inference_dir: 转换后模型的保存路径；
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* Arch: 模型结构相关定义，可参考 [配置说明](../models_training/config_description.md#3-%E8%AF%86%E5%88%AB%E6%A8%A1%E5%9E%8B)；
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* IndexProcess: 底库数据集相关定义
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* image_root: 底库数据集路径；
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* data_file: 底库数据集列表文件路径；
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### 1.3 转换特征提取模型
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在完成上述准备工作后，即可进行模型转换，命令如下所示：
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```python
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python ppcls/utils/gallery2fc.py -c ppcls/configs/GeneralRecognition/Gallery2FC_PPLCNet_x2_5.yaml
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```
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在上述命令执行完成后，转换并导出的模型保存在目录 `./inference/general_PPLCNet_x2_5_quant/` 下。在推理部署时，需要注意的是，模型的输出结果通常有多个，应选取分类结果作为模型输出，需要注意区分。
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# global configs
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Global:
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pretrained_model: ./pretrained/general_PPLCNet_x2_5_pretrained_v1.0_quant
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# used for static mode and model export
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image_shape: [3, 224, 224]
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save_inference_dir: ./inference/general_PPLCNet_x2_5_quant/inference
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# for quantizaiton or prune model
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Slim:
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## for prune
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quant:
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name: pact
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# model architecture
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Arch:
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name: RecModel
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Backbone:
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name: PPLCNet_x2_5
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pretrained: False
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use_ssld: True
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BackboneStopLayer:
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name: "flatten"
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Neck:
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name: FC
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embedding_size: 1280
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class_num: 512
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Head:
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name: ArcMargin
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embedding_size: 512
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class_num: 185341
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margin: 0.2
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scale: 30
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# indexing engine config
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IndexProcess:
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image_root: "./drink_dataset_v1.0/gallery/"
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data_file: "./drink_dataset_v1.0/gallery/drink_label.txt"
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delimiter: "\t"
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batch_size: 2
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transform_ops:
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [ 0.485, 0.456, 0.406 ]
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std: [ 0.229, 0.224, 0.225 ]
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order: ''
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- ToCHWImage:
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@ -12,10 +12,14 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import paddle
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import cv2
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import os
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import sys
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
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from ppcls.arch import build_model
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from ppcls.utils.config import parse_config, parse_args
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from ppcls.utils.save_load import load_dygraph_pretrain
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@ -51,12 +55,17 @@ class GalleryLayer(paddle.nn.Layer):
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self.gallery_images.append(image_file)
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gallery_docs.append(ori_line.strip())
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gallery_labels.append(line[1].strip())
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self.gallery_layer = paddle.nn.Linear(embedding_size, len(self.gallery_images), bias_attr=False)
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self.gallery_layer = paddle.nn.Linear(
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embedding_size, len(self.gallery_images), bias_attr=False)
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self.gallery_layer.skip_quant = True
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output_label_str = ""
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for i, label_i in enumerate(gallery_labels):
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output_label_str += "{} {}\n".format(i, label_i)
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output_path = configs["Global"]["save_inference_dir"] + "_label.txt"
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save_dir = os.path.dirname(configs["Global"]["save_inference_dir"])
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if not os.path.exists(save_dir):
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os.makedirs(save_dir)
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with open(output_path, "w") as f:
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f.write(output_label_str)
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@ -71,19 +80,23 @@ class GalleryLayer(paddle.nn.Layer):
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embedding_size = self.configs["Arch"]["Head"]["embedding_size"]
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batch_index = 0
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input_tensor = paddle.zeros(self.image_shape)
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gallery_feature = paddle.zeros((len(self.gallery_images), embedding_size))
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gallery_feature = paddle.zeros(
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(len(self.gallery_images), embedding_size))
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for i, image_path in enumerate(self.gallery_images):
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image = cv2.imread(image_path)[:, :, ::-1]
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for op in preprocess_ops:
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image = op(image)
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input_tensor[batch_index] = image
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batch_index += 1
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if batch_index == self.batch_size or i == len(self.gallery_images) - 1:
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if batch_index == self.batch_size or i == len(
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self.gallery_images) - 1:
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batch_feature = feature_extractor(input_tensor)["features"]
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for j in range(batch_index):
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feature = batch_feature[j]
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norm_feature = paddle.nn.functional.normalize(feature, axis=0)
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norm_feature = paddle.nn.functional.normalize(
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feature, axis=0)
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gallery_feature[i - batch_index + j + 1] = norm_feature
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batch_index = 0
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self.gallery_layer.set_state_dict({"_layer.weight": gallery_feature.T})
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