torch_verify

.gitignore

+**/log
\ No newline at end of file

dynamic_to_static/install.sh

+#!/bin/bash
+
+pip3 config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
+pip3 install onnx-simplifier
+pip3 install paddle2onnx
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dynamic_to_static/readme.md

+## 方法一
+
+使用 `onnxsim` 转换;
+
+安装包的步骤
+```bash
+pip install onnx-simplifier
+```
+
+```python
+python -m onnxsim ../resnet50.onnx ../resnet50_1x3x224x224.onnx --test-input-shape input:1,3,224,224 
+
+
+# 查看详细帮助内容
+python -m onnxsim -h
+
+```
+
+## 方法二
+
+使用 `paddle2onnx` 去进行转换。
+
+```python
+pip3 install paddle2onnx
+
+python -m paddle2onnx.optimize --input_model model.onnx \
+--output_model new_model.onnx \
+--input_shape_dict "{'x':[1,3,224,224]}"
+```
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migraphx/run.sh

-# --fp16：使用半精度
-# --onnx：模型路径
-# --input-dim：模型输入维度
-#	@unique_ids_raw_output___9:0 1：标志位无需修改
-#	@segment_ids:0 256 256：第一个数为batch size，默认256；第二个数为序列长度，默认256
-#       @input_mask:0 256 256：第一个数为batch size，默认256；第二个数为序列长度，默认256
-#	@input_ids:0 256 256：第一个数为batch size，默认256；第二个数为序列长度，默认256
-
-
 data=$(date +"%Y-%m-%d-%H-%M-%S")
-logdir=/datasets/logs_comomm_model_infer/bert/${data}
+logdir=./log/${data}
 
 if [ ! -f ${logdir} ]; then
     mkdir ${logdir} -p
 fi
 
-HIP_VISIBLE_DEVICES=0 /opt/dtk-24.04.1/bin/migraphx-driver perf -n 20 --fp16 --gpu --onnx ./weights/bertsquad-10.onnx \
-	--input-dim @unique_ids_raw_output___9:0 1 @segment_ids:0 256 256 @input_mask:0 256 256 @input_ids:0 256 256 \
-	2>&1 | tee ${logdir}/pert-bert-bs-256-seqlen-256.log
+HIP_VISIBLE_DEVICES=0 \
+/opt/dtk-24.04.1/bin/migraphx-driver perf -n 20 --fp16 --gpu \
+--onnx ../resnet50.onnx \
+--input-dim @input 1 3 224 224 \
+2>&1 | tee ${logdir}/resnet.log

onnxruntime-rocm/run.sh

+data=$(date +"%Y-%m-%d-%H-%M-%S")
+logdir=./log/${data}
+
+if [ ! -f ${logdir} ]; then
+    mkdir ${logdir} -p
+fi
+
+onnxruntime_perf_test -m times \
+-e rocm \
+-r 20 \
+-o 1 \
+-I \
+../resnet50_1x3x224x224.onnx \
+2>&1 | tee ${logdir}/resnet.log
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package/download_link.txt

+下载地址： 
+
+https://cancon.hpccube.com:65024/4/main/migraphx
+
+
+安装：
+
+chmod +x migraphx.run
+./migraphx.run
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torch_verify.py

@@ -46,3 +46,24 @@ with open(synset_words_path, 'r') as f:
 class_names = [line.strip() for line in synset_words]
 predicted_class_name = class_names[predicted_class_idx+1]  # 注意索引可能从1开始，所以+1
 print(f"Predicted class name: {predicted_class_name}")  # 彭布罗克，彭布罗克威尔士柯基犬
+
+with torch.no_grad():
+    input_tensor = torch.ones((1,3,224,224), dtype=torch.float).to("cuda:0")
+    save_path = "./resnet50.onnx"
+    dynamic_shape = {
+        'input': {0: 'batch_size', 2: 'height', 3: 'width'},
+        'output': {0: 'batch_size', 1: 'n_class'}
+    }
+
+    torch.onnx.export(
+        model, 
+        (input_tensor, ),
+        f=save_path,
+        verbose=False,
+        opset_version=17,
+        input_names=['input'],
+        output_names=['output'],
+        dynamic_axes=dynamic_shape,
+    )
+
+    print("export success")
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