无法复现教程中的OCR v3检测模型的量化精度

[TinyQi]

我使用文档中：https://github.com/PaddlePaddle/PaddleSlim/tree/release/2.6/example/auto_compression/ocr的量化脚本进行量化，经过训练后模型的精度下降很大，请问我哪里做错了嘛？

精度的前后对比如下：

我使用的配置文件如下：
Global:
model_type: det
model_dir: /share/disk3/xcq/02.model_cache/pretrain_models/ch_PP-OCRv3_det_infer_bak/
model_filename: inference.pdmodel
params_filename: inference.pdiparams
algorithm: DB

Distillation:
alpha: 1.0
loss: l2

QuantAware:
use_pact: true
activation_bits: 8
is_full_quantize: false
onnx_format: True
activation_quantize_type: moving_average_abs_max
weight_quantize_type: channel_wise_abs_max
not_quant_pattern:

• skip_quant
  quantize_op_types:
• conv2d
• depthwise_conv2d
  weight_bits: 8

TrainConfig:
epochs: 3
eval_iter: 200
learning_rate:
type: CosineAnnealingDecay
learning_rate: 0.00005
optimizer_builder:
optimizer:
type: Adam
weight_decay: 5.0e-05

PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5

Metric:
name: DetMetric
main_indicator: hmean

Train:
dataset:
name: SimpleDataSet
data_dir: /share/disk3/xcq/01.ImageData/010.OpenSourceData/icdar2015/text_localization/
label_file_list:
- /share/disk3/xcq/01.ImageData/010.OpenSourceData/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- DetLabelEncode: null
- IaaAugment:
augmenter_args:
- type: Fliplr
args:
p: 0.5
- type: Affine
args:
rotate:
- -10
- 10
- type: Resize
args:
size:
- 0.5
- 3
- EastRandomCropData:
size:
- 960
- 960
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- NormalizeImage:
scale: 1./255.
mean:
- 0.485
- 0.456
- 0.406
std:
- 0.229
- 0.224
- 0.225
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- threshold_map
- threshold_mask
- shrink_map
- shrink_mask
loader:
shuffle: true
drop_last: false
batch_size_per_card: 4
num_workers: 0

Eval:
dataset:
name: SimpleDataSet
data_dir: /share/disk3/xcq/01.ImageData/010.OpenSourceData/icdar2015/text_localization/
label_file_list:
- /share/disk3/xcq/01.ImageData/010.OpenSourceData/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage:
img_mode: BGR
channel_first: false
- DetLabelEncode: null
- DetResizeForTest: null
- NormalizeImage:
scale: 1./255.
mean:
- 0.485
- 0.456
- 0.406
std:
- 0.229
- 0.224
- 0.225
order: hwc
- ToCHWImage: null
- KeepKeys:
keep_keys:
- image
- shape
- polys
- ignore_tags
loader:
shuffle: false
drop_last: false
batch_size_per_card: 1
num_workers: 0

[ceci3]

我看上一个issue的log看起来是量化训练，可以参考 https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/post_training_quantization/detection/post_quant.py#L75 这个示例使用离线量化看下效果