debug

ppcls/configs/ImageNet/PPHGNet/PPHGNet_small_pact.yaml

-# global configs
-Global:
-  checkpoints: null
-  pretrained_model: null
-  output_dir: ./output/
-  device: gpu
-  save_interval: 1
-  eval_during_train: True
-  eval_interval: 1
-  epochs: 600
-  print_batch_step: 10
-  use_visualdl: False
-  # used for static mode and model export
-  image_shape: [3, 224, 224]
-  save_inference_dir: ./inference
-  # training model under @to_static
-  to_static: False
-  use_dali: False
-
-# model architecture
-Arch:
-  name: PPHGNet_small
-  class_num: 1000
- 
-# loss function config for traing/eval process
-Loss:
-  Train:
-    - CELoss:
-        weight: 1.0
-        epsilon: 0.1
-  Eval:
-    - CELoss:
-        weight: 1.0
-
-
-Optimizer:
-  name: Momentum
-  momentum: 0.9
-  lr:
-    name: Cosine
-    learning_rate: 0.5
-    warmup_epoch: 5
-  regularizer:
-    name: 'L2'
-    coeff: 0.00004
-
-
-# data loader for train and eval
-DataLoader:
-  Train:
-    dataset:
-      name: ImageNetDataset
-      image_root: ./dataset/ILSVRC2012/
-      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
-      transform_ops:
-        - DecodeImage:
-            to_rgb: True
-            channel_first: False
-        - RandCropImage:
-            size: 224
-            interpolation: bicubic
-            backend: pil
-        - RandFlipImage:
-            flip_code: 1
-        - TimmAutoAugment:
-            config_str: rand-m7-mstd0.5-inc1
-            interpolation: bicubic
-            img_size: 224
-        - NormalizeImage:
-            scale: 1.0/255.0
-            mean: [0.485, 0.456, 0.406]
-            std: [0.229, 0.224, 0.225]
-            order: ''
-        - RandomErasing:
-            EPSILON: 0.25
-            sl: 0.02
-            sh: 1.0/3.0
-            r1: 0.3
-            attempt: 10
-            use_log_aspect: True
-            mode: pixel
-      batch_transform_ops:
-        - OpSampler:
-            MixupOperator:
-              alpha: 0.2
-              prob: 0.5
-            CutmixOperator:
-              alpha: 1.0
-              prob: 0.5
-
-    sampler:
-      name: DistributedBatchSampler
-      batch_size: 128
-      drop_last: False
-      shuffle: True
-    loader:
-      num_workers: 16
-      use_shared_memory: True
-
-  Eval:
-    dataset: 
-      name: ImageNetDataset
-      image_root: ./dataset/ILSVRC2012/
-      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
-      transform_ops:
-        - DecodeImage:
-            to_rgb: True
-            channel_first: False
-        - ResizeImage:
-            resize_short: 236
-            interpolation: bicubic
-            backend: pil
-        - CropImage:
-            size: 224
-        - NormalizeImage:
-            scale: 1.0/255.0
-            mean: [0.485, 0.456, 0.406]
-            std: [0.229, 0.224, 0.225]
-            order: ''
-    sampler:
-      name: DistributedBatchSampler
-      batch_size: 128
-      drop_last: False
-      shuffle: False
-    loader:
-      num_workers: 16
-      use_shared_memory: True
-
-Infer:
-  infer_imgs: docs/images/inference_deployment/whl_demo.jpg
-  batch_size: 10
-  transforms:
-    - DecodeImage:
-        to_rgb: True
-        channel_first: False
-    - ResizeImage:
-        resize_short: 236
-    - CropImage:
-        size: 224
-    - NormalizeImage:
-        scale: 1.0/255.0
-        mean: [0.485, 0.456, 0.406]
-        std: [0.229, 0.224, 0.225]
-        order: ''
-    - ToCHWImage:
-  PostProcess:
-    name: Topk
-    topk: 5
-    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
-
-Metric:
-  Train:
-    - TopkAcc:
-        topk: [1, 5]
-  Eval:
-    - TopkAcc:
-        topk: [1, 5]

test_tipc/docs/test_train_ptq_inference_python.md

+# Linux GPU/CPU KL离线量化推理测试
+
+Linux GPU/CPU KL离线量化推理测试的主程序为`test_ptq_inference_python.sh`，可以测试基于Python的模型KL离线量化推理等基本功能。
+
+## 1. 测试结论汇总
+
+- KL离线量化：
+
+|    算法名称     |                模型名称                 |   CPU   |
+| :-------------: | :-------------------------------------: | :----------: |
+|   MobileNetV3   |         MobileNetV3_large_x1_0          | KL离线量化 |
+|    PP-ShiTu     |     GeneralRecognition_PPLCNet_x2_5     | KL离线量化 |
+|     PPHGNet     |              PPHGNet_small              | KL离线量化 |
+|     PPHGNet     |              PPHGNet_tiny               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x0_25              | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x0_35              | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x0_5               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x0_75              | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x1_0               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x1_5               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x2_0               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x2_5               | KL离线量化 |
+|    PPLCNetV2    |             PPLCNetV2_base              | KL离线量化 |
+|     ResNet      |                ResNet50                 | KL离线量化 |
+|     ResNet      |               ResNet50_vd               | KL离线量化 |
+| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | KL离线量化 |
+
+- 推理相关：
+
+|    算法名称     |                模型名称                 |   CPU   |
+| :-------------: | :-------------------------------------: | :----------: |
+|   MobileNetV3   |         MobileNetV3_large_x1_0          | KL离线量化 |
+|    PP-ShiTu     |     GeneralRecognition_PPLCNet_x2_5     | KL离线量化 |
+|     PPHGNet     |              PPHGNet_small              | KL离线量化 |
+|     PPHGNet     |              PPHGNet_tiny               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x0_25              | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x0_35              | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x0_5               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x0_75              | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x1_0               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x1_5               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x2_0               | KL离线量化 |
+|     PPLCNet     |              PPLCNet_x2_5               | KL离线量化 |
+|    PPLCNetV2    |             PPLCNetV2_base              | KL离线量化 |
+|     ResNet      |                ResNet50                 | KL离线量化 |
+|     ResNet      |               ResNet50_vd               | KL离线量化 |
+| SwinTransformer | SwinTransformer_tiny_patch4_window7_224 | KL离线量化 |
+
+
+## 2. 测试流程
+
+一下测试流程以 MobileNetV3_large_x1_0 模型为例。
+
+### 2.1 准备环境
+
+- 安装PaddlePaddle：如果您已经安装了2.2或者以上版本的paddlepaddle，那么无需运行下面的命令安装paddlepaddle。
+  ```bash
+  # 需要安装2.2及以上版本的Paddle
+  # 安装GPU版本的Paddle
+  python3.7 -m pip install paddlepaddle-gpu==2.2.0
+  # 安装CPU版本的Paddle
+  python3.7 -m pip install paddlepaddle==2.2.0
+  ```
+
+- 安装PaddleSlim
+  ```bash
+  python3.7 -m pip install paddleslim==2.2.0
+  ```
+
+- 安装依赖
+  ```bash
+  python3.7 -m pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+  ```
+
+- 安装AutoLog（规范化日志输出工具）
+  ```bash
+  python3.7 -m pip install https://paddleocr.bj.bcebos.com/libs/auto_log-1.2.0-py3-none-any.whl
+  ```
+
+### 2.2 准备数据和模型
+
+```bash
+bash test_tipc/prepare.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
+```
+
+离线量化的操作流程，可参考[文档](../../deploy/slim/README.md)。
+
+### 2.3 功能测试
+
+以`MobileNetV3_large_x1_0`的`Linux GPU/CPU KL离线量化训练推理测试`为例，命令如下所示。
+
+```bash
+bash test_tipc/test_ptq_inference_python.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_ptq_infer_python.txt whole_infer
+```
+
+输出结果如下，表示命令运行成功。
+
+```log
+Run successfully with command - MobileNetV3_large_x1_0 - python3.7 deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./MobileNetV3_large_x1_0_infer!
+Run successfully with command - MobileNetV3_large_x1_0 - python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=True -o Global.use_tensorrt=False -o Global.use_fp16=False -o Global.inference_model_dir=.././MobileNetV3_large_x1_0_infer//quant_post_static_model -o Global.batch_size=1 -o Global.infer_imgs=../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg -o Global.benchmark=False > .././test_tipc/output/MobileNetV3_large_x1_0/whole_infer/infer_gpu_usetrt_False_precision_False_batchsize_1.log 2>&1 !
+Run successfully with command - MobileNetV3_large_x1_0 - python3.7 python/predict_cls.py -c configs/inference_cls.yaml -o Global.use_gpu=False -o Global.enable_mkldnn=False -o Global.cpu_num_threads=1 -o Global.inference_model_dir=.././MobileNetV3_large_x1_0_infer//quant_post_static_model -o Global.batch_size=1 -o Global.infer_imgs=../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg -o Global.benchmark=False   > .././test_tipc/output/MobileNetV3_large_x1_0/whole_infer/infer_cpu_usemkldnn_False_threads_1_batchsize_1.log 2>&1 !
+```
+同时，测试过程中的日志保存在`PaddleClas/test_tipc/output/MobileNetV3_large_x1_0/whole_infer`下。
+
+如果运行失败，也会在终端中输出运行失败的日志信息以及对应的运行命令。可以基于该命令，分析运行失败的原因。

test_tipc/test_ptq_inference_python.sh

+#!/bin/bash
+FILENAME=$1
+source test_tipc/common_func.sh
+
+# MODE be one of ['whole_infer']
+MODE=$2
+
+dataline=$(cat ${FILENAME})
+
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+
+# The training params
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+gpu_list=$(func_parser_value "${lines[3]}")
+train_use_gpu_key=$(func_parser_key "${lines[4]}")
+train_use_gpu_value=$(func_parser_value "${lines[4]}")
+autocast_list=$(func_parser_value "${lines[5]}")
+autocast_key=$(func_parser_key "${lines[5]}")
+epoch_key=$(func_parser_key "${lines[6]}")
+epoch_num=$(func_parser_params "${lines[6]}")
+save_model_key=$(func_parser_key "${lines[7]}")
+train_batch_key=$(func_parser_key "${lines[8]}")
+train_batch_value=$(func_parser_value "${lines[8]}")
+pretrain_model_key=$(func_parser_key "${lines[9]}")
+pretrain_model_value=$(func_parser_value "${lines[9]}")
+train_model_name=$(func_parser_value "${lines[10]}")
+train_infer_img_dir=$(func_parser_value "${lines[11]}")
+train_param_key1=$(func_parser_key "${lines[12]}")
+train_param_value1=$(func_parser_value "${lines[12]}")
+
+trainer_list=$(func_parser_value "${lines[14]}")
+trainer_norm=$(func_parser_key "${lines[15]}")
+norm_trainer=$(func_parser_value "${lines[15]}")
+pact_key=$(func_parser_key "${lines[16]}")
+pact_trainer=$(func_parser_value "${lines[16]}")
+fpgm_key=$(func_parser_key "${lines[17]}")
+fpgm_trainer=$(func_parser_value "${lines[17]}")
+distill_key=$(func_parser_key "${lines[18]}")
+distill_trainer=$(func_parser_value "${lines[18]}")
+to_static_key=$(func_parser_key "${lines[19]}")
+to_static_trainer=$(func_parser_value "${lines[19]}")
+trainer_key2=$(func_parser_key "${lines[20]}")
+trainer_value2=$(func_parser_value "${lines[20]}")
+
+eval_py=$(func_parser_value "${lines[23]}")
+eval_key1=$(func_parser_key "${lines[24]}")
+eval_value1=$(func_parser_value "${lines[24]}")
+
+save_infer_key=$(func_parser_key "${lines[27]}")
+export_weight=$(func_parser_key "${lines[28]}")
+norm_export=$(func_parser_value "${lines[29]}")
+pact_export=$(func_parser_value "${lines[30]}")
+fpgm_export=$(func_parser_value "${lines[31]}")
+distill_export=$(func_parser_value "${lines[32]}")
+kl_quant_cmd_key=$(func_parser_key "${lines[33]}")
+kl_quant_cmd_value=$(func_parser_value "${lines[33]}")
+export_key2=$(func_parser_key "${lines[34]}")
+export_value2=$(func_parser_value "${lines[34]}")
+
+# parser inference model
+infer_model_dir_list=$(func_parser_value "${lines[36]}")
+infer_export_flag=$(func_parser_value "${lines[37]}")
+infer_is_quant=$(func_parser_value "${lines[38]}")
+
+# parser inference
+inference_py=$(func_parser_value "${lines[39]}")
+use_gpu_key=$(func_parser_key "${lines[40]}")
+use_gpu_list=$(func_parser_value "${lines[40]}")
+use_mkldnn_key=$(func_parser_key "${lines[41]}")
+use_mkldnn_list=$(func_parser_value "${lines[41]}")
+cpu_threads_key=$(func_parser_key "${lines[42]}")
+cpu_threads_list=$(func_parser_value "${lines[42]}")
+batch_size_key=$(func_parser_key "${lines[43]}")
+batch_size_list=$(func_parser_value "${lines[43]}")
+use_trt_key=$(func_parser_key "${lines[44]}")
+use_trt_list=$(func_parser_value "${lines[44]}")
+precision_key=$(func_parser_key "${lines[45]}")
+precision_list=$(func_parser_value "${lines[45]}")
+infer_model_key=$(func_parser_key "${lines[46]}")
+image_dir_key=$(func_parser_key "${lines[47]}")
+infer_img_dir=$(func_parser_value "${lines[47]}")
+save_log_key=$(func_parser_key "${lines[48]}")
+benchmark_key=$(func_parser_key "${lines[49]}")
+benchmark_value=$(func_parser_value "${lines[49]}")
+infer_key1=$(func_parser_key "${lines[50]}")
+infer_value1=$(func_parser_value "${lines[50]}")
+if [ ! $epoch_num ]; then
+    epoch_num=2
+fi
+if [[ $MODE = 'benchmark_train' ]]; then
+    epoch_num=1
+fi
+
+LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results_python.log"
+
+function func_inference() {
+    IFS='|'
+    _python=$1
+    _script=$2
+    _model_dir=$3
+    _log_path=$4
+    _img_dir=$5
+    _flag_quant=$6
+    # inference
+    for use_gpu in ${use_gpu_list[*]}; do
+        if [ ${use_gpu} = "False" ] || [ ${use_gpu} = "cpu" ]; then
+            for use_mkldnn in ${use_mkldnn_list[*]}; do
+                for threads in ${cpu_threads_list[*]}; do
+                    for batch_size in ${batch_size_list[*]}; do
+                        _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}.log"
+                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
+                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
+                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
+                        set_cpu_threads=$(func_set_params "${cpu_threads_key}" "${threads}")
+                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                        set_infer_params1=$(func_set_params "${infer_key1}" "${infer_value1}")
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${set_cpu_threads} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} ${set_infer_params1} > ${_save_log_path} 2>&1 "
+                        eval $command
+                        last_status=${PIPESTATUS[0]}
+                        eval "cat ${_save_log_path}"
+                        status_check $last_status "${command}" "../${status_log}" "${model_name}"
+                    done
+                done
+            done
+        elif [ ${use_gpu} = "True" ] || [ ${use_gpu} = "gpu" ]; then
+            for use_trt in ${use_trt_list[*]}; do
+                for precision in ${precision_list[*]}; do
+                    if [ ${precision} = "True" ] && [ ${use_trt} = "False" ]; then
+                        continue
+                    fi
+                    for batch_size in ${batch_size_list[*]}; do
+                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
+                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
+                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
+                        set_batchsize=$(func_set_params "${batch_size_key}" "${batch_size}")
+                        set_tensorrt=$(func_set_params "${use_trt_key}" "${use_trt}")
+                        set_precision=$(func_set_params "${precision_key}" "${precision}")
+                        set_model_dir=$(func_set_params "${infer_model_key}" "${_model_dir}")
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${set_tensorrt} ${set_precision} ${set_model_dir} ${set_batchsize} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
+                        eval $command
+                        last_status=${PIPESTATUS[0]}
+                        eval "cat ${_save_log_path}"
+                        status_check $last_status "${command}" "../${status_log}" "${model_name}"
+                    done
+                done
+            done
+        else
+            echo "Does not support hardware other than CPU and GPU Currently!"
+        fi
+    done
+}
+
+# for kl_quant
+if [ ${kl_quant_cmd_value} != "null" ] && [ ${kl_quant_cmd_value} != "False" ]; then
+    echo "kl_quant"
+    command="${python} ${kl_quant_cmd_value}"
+    echo ${command}
+    eval $command
+    last_status=${PIPESTATUS[0]}
+    status_check $last_status "${command}" "${status_log}" "${model_name}"
+    cd ${infer_model_dir_list}/quant_post_static_model
+    ln -s __model__ inference.pdmodel
+    ln -s __params__ inference.pdiparams
+    cd ../../deploy
+    is_quant=True
+    func_inference "${python}" "${inference_py}" "../${infer_model_dir_list}/quant_post_static_model" "../${LOG_PATH}" "${infer_img_dir}" ${is_quant}
+    cd ..
+fi