dev

inference/benchmarks/yolov5/README.md

@@ -30,7 +30,7 @@ find ./val -name "*JPEG" | wc -l
 ### 2. 模型与权重
 
 * 模型实现
-  * pytorch：yolov5l-bs64.onnx
+  * pytorch：yolov5l-bs96.onnx
 * 权重下载
   * pytorch：https://model.baai.ac.cn/model-detail/100097
 
@@ -76,5 +76,4 @@ find ./val -name "*JPEG" | wc -l
 
 | 推理工具  | precision | bs   | e2e_time | p_val_whole | \*p_val_core | p_infer_whole | \*p_infer_core | acc         | mem        |
 | ----------- | --------- | ---- | -------- | ----------- | ---------- | ------------- | ------------ | ----------- | ---------- |
-| tensorrt | fp16      | 256  | 613.4 | 1358.9   | 4263.3 | 1391.4   | 11483.0 | 76.2/76.2 | 19.7/40.0 |
-| tensorrt | fp32   | 256  | 474.4    | 1487.3      | 2653.2     | 1560.3        | 6091.6  | 76.2/76.2 | 28.86/40.0 |
+| tensorrt | fp32   | 96  | 733.8    |    /   | /    | 53.8       | 361.4 | 0.45 | 35.44/40.0 |

inference/configs/yolov5/configurations.yaml

@@ -1,14 +1,13 @@
-batch_size: 1
-# 3*224*224(1 item in x)
-input_size: 409600
+batch_size: 128
 fp16: False
+flops: 5.45e10
 compiler: tensorrt
 num_workers: 8
 log_freq: 30
-repeat: 1
+repeat: 5
 # skip validation(will also skip create_model, export onnx). Assert exist_onnx_path != null
 no_validation: True
 # set a real onnx_path to use exist, or set it to anything but null to avoid export onnx manually(like torch-tensorrt)
-exist_onnx_path: True
+exist_onnx_path: /FlagPerf/inference/yolov5l-bs128.onnx
 # set a exist path of engine file like resnet50.trt/resnet50.plan/resnet50.engine
-exist_compiler_path: /workspace/inference/yolov5l-cor.engine
+exist_compiler_path: null

inference/configs/yolov5/parameters.yaml

@@ -1,8 +1,7 @@
 # contain case-specified parameters, like max_seq_length in BERT. 
 # There is no parameters for resnet50 case.
 conf_thres: 0.05
-iou_thres: 0.2
+iou_thres: 0.2 
 max_det: 1000
 number_boxes: 85
 hidden_size: 25200
-