pavel.lamonov
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89 lines
5.0 KiB
Bash
89 lines
5.0 KiB
Bash
#!/bin/bash
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# 本脚本用于测试PPOCRV4_det系列模型的自动压缩功能
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## 运行脚本前，请确保处于以下环境：
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## CUDA11.7+TensorRT8.4.2.4+Paddle2.5.2
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model_type="$1"
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if [ "$model_type" = "mobile" ]; then
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echo "test ppocrv4_det_mobile model......"
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## 启动自动化压缩训练
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CUDA_VISIBLE_DEVICES=0 python run.py --save_dir ./models/det_mobile_qat --config_path configs/ppocrv4/ppocrv4_det_qat_dist.yaml
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## GPU指标测试
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### 量化前，预期指标：hmean:72.71%;time:4.7ms
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python test_ocr.py --model_path ./models/ch_PP-OCRv4_det_infer --config ./configs/ppocrv4/ppocrv4_det_qat_dist.yaml --precision fp32 --use_trt True
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### 量化后，预期指标：hmean:71.38%;time:3.3ms
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python test_ocr.py --model_path ./models/det_mobile_qat --config ./configs/ppocrv4/ppocrv4_det_qat_dist.yaml --precision int8 --use_trt True
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## CPU指标测试
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### 量化前，预期指标：hmean:72.71%;time:198.4ms
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python test_ocr.py --model_path ./models/ch_PP-OCRv4_det_infer --config ./configs/ppocrv4/ppocrv4_det_qat_dist.yaml --precision fp32 --use_mkldnn True --device CPU --cpu_threads 12
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### 量化后，预期指标：hmean:72.30%;time:205.2ms
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python test_ocr.py --model_path ./models/det_mobile_qat --config ./configs/ppocrv4/ppocrv4_det_qat_dist.yaml --precision int8 --use_mkldnn True --device CPU --cpu_threads 12
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# 量化前模型推理
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# GPU
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python tools/infer/predict_det.py --det_model_dir deploy/slim/auto_compression/models/ch_PP-OCRv4_det_infer \
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--benchmark True --image_dir deploy/slim/auto_compression/datasets/v4_4_test_dataset --use_gpu True \
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--use_tensorrt True --warmup True --precision fp32
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# CPU
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python tools/infer/predict_det.py --det_model_dir deploy/slim/auto_compression/models/ch_PP-OCRv4_det_infer \
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--benchmark True --image_dir deploy/slim/auto_compression/datasets/v4_4_test_dataset --use_gpu False \
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--enable_mkldnn True --warmup True --precision fp32
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# 量化后模型推理
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# GPU
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python tools/infer/predict_det.py --det_model_dir deploy/slim/auto_compression/models/det_mobile_qat \
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--benchmark True --image_dir deploy/slim/auto_compression/datasets/v4_4_test_dataset --use_gpu True \
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--use_tensorrt True --warmup True --precision int8
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# CPU
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python tools/infer/predict_det.py --det_model_dir deploy/slim/auto_compression/models/det_mobile_qat \
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--benchmark True --image_dir deploy/slim/auto_compression/datasets/v4_4_test_dataset --use_gpu False \
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--enable_mkldnn True --warmup True --precision int8
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elif [ "$model_type" = "server" ]; then
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echo "test ppocrv4_det_server model......"
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## 启动自动化压缩训练
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CUDA_VISIBLE_DEVICES=0 python run.py --save_dir ./models/det_server_qat --config_path configs/ppocrv4/ppocrv4_det_server_qat_dist.yaml
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## GPU指标测试
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### 量化前，预期指标：hmean:79.77%;time:50.0ms
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python test_ocr.py --model_path ./models/ch_PP-OCRv4_det_server_infer --config ./configs/ppocrv4/ppocrv4_det_server_qat_dist.yaml --precision fp32 --use_trt True
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### 量化后，预期指标：hmean:79.81%;time:42.4ms
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python test_ocr.py --model_path ./models/det_server_qat --config ./configs/ppocrv4/ppocrv4_det_server_qat_dist.yaml --precision int8 --use_trt True
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## CPU指标测试
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### 量化前，预期指标：hmean:79.77%;time:2159.4ms
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python test_ocr.py --model_path ./models/ch_PP-OCRv4_det_server_infer --config ./configs/ppocrv4/ppocrv4_det_server_qat_dist.yaml --precision fp32 --use_mkldnn True --device CPU --cpu_threads 12
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### 量化后，预期指标：hmean:79.69%;time:1834.8ms
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python test_ocr.py --model_path ./models/det_server_qat --config ./configs/ppocrv4/ppocrv4_det_server_qat_dist.yaml --precision int8 --use_mkldnn True --device CPU --cpu_threads 12
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## 量化前模型推理
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### GPU
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python tools/infer/predict_det.py --det_model_dir deploy/slim/auto_compression/models/ch_PP-OCRv4_det_server_infer \
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--benchmark True --image_dir deploy/slim/auto_compression/datasets/v4_4_test_dataset --use_gpu True \
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--use_tensorrt True --warmup True --precision fp32
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### CPU
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python tools/infer/predict_det.py --det_model_dir deploy/slim/auto_compression/models/ch_PP-OCRv4_det_server_infer \
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--benchmark True --image_dir deploy/slim/auto_compression/datasets/v4_4_test_dataset --use_gpu False \
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--enable_mkldnn True --warmup True --precision fp32
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## 量化后模型推理
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### GPU
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python tools/infer/predict_det.py --det_model_dir deploy/slim/auto_compression/models/det_server_qat \
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--benchmark True --image_dir deploy/slim/auto_compression/datasets/v4_4_test_dataset --use_gpu True \
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--use_tensorrt True --warmup True --precision int8
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### CPU
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python tools/infer/predict_det.py --det_model_dir deploy/slim/auto_compression/models/det_server_qat \
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--benchmark True --image_dir deploy/slim/auto_compression/datasets/v4_4_test_dataset --use_gpu False \
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--enable_mkldnn True --warmup True --precision int8
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else
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echo "unrecgnized model_type"
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fi
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