EngineX-Iluvatar/enginex-bi_series-vc-cnn
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zhousha
2025-08-06 15:45:17 +08:00
parent 55a67e817e
commit 44954b7481
1 changed files with 4 additions and 208 deletions
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212
run_callback.py
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@@ -111,46 +111,6 @@ def get_sut_url_kubernetes():
requests = submit_config["values"]["resources"]["requests"]
"""
# ########## 关键修改：替换为iluvatar GPU配置 ##########
if RESOURCE_TYPE == "vgpu": # 假设你的模型需要GPU
# 替换nvidia资源键为iluvatar.ai/gpu
vgpu_resource = {
"iluvatar.ai/gpu": SUT_VGPU, # 对应你的GPU资源键
# 若需要其他资源（如显存），按你的K8s配置补充，例如：
# "iluvatar.ai/gpumem": SUT_VGPU_MEM,
}
limits.update(vgpu_resource)
requests.update(vgpu_resource)
# 节点选择器：替换为你的accelerator标签
submit_config["values"]["nodeSelector"] = {
"contest.4pd.io/accelerator": "iluvatar-BI-V100" # 你的节点标签
}
# 容忍度：替换为你的tolerations配置
submit_config["values"]["tolerations"] = [
{
"key": "hosttype",
"operator": "Equal",
"value": "iluvatar",
"effect": "NoSchedule",
}
]
# #########################################
# 禁止CPU模式下使用GPU资源（保持原逻辑）
else:
if "iluvatar.ai/gpu" in limits or "iluvatar.ai/gpu" in requests:
log.error("禁止在CPU模式下使用GPU资源")
sys.exit(1)
#gpukeys = ["iluvatar.ai/gpu"] # 检查iluvatar GPU键
#for key in gpukeys:
# if key in limits or key in requests:
# log.error("禁止使用vgpu资源")
# sys.exit(1)
"""
# 替换nvidia资源键为iluvatar.ai/gpu
vgpu_resource = {
@@ -165,54 +125,8 @@ def get_sut_url_kubernetes():
"contest.4pd.io/accelerator": "iluvatar-BI-V100" # 你的节点标签
}
# 容忍度：替换为你的tolerations配置
"""
submit_config["values"]["tolerations"] = [
{
"key": "hosttype",
"operator": "Equal",
"value": "iluvatar",
"effect": "NoSchedule",
},
{
"key": "hosttype",
"operator": "Equal",
"value": "arm64",
"effect": "NoSchedule",
},
{
"key": "hosttype",
"operator": "Equal",
"value": "myinit",
"effect": "NoSchedule",
},
{
"key": "hosttype",
"operator": "Equal",
"value": "middleware",
"effect": "NoSchedule",
}
]
"""
"""
{
"key": "node-role.kubernetes.io/master",
"operator": "Exists",
"effect": "NoSchedule",
},
{
"key": "node.kubernetes.io/not-ready",
"operator": "Exists",
"effect": "NoExecute",
"tolerationSeconds": 300
},
{
"key": "node.kubernetes.io/unreachable",
"operator": "Exists",
"effect": "NoExecute",
"tolerationSeconds": 300
}
"""
log.info(f"submit_config: {submit_config}")
@@ -629,46 +543,6 @@ if __name__ == '__main__':
response = requests.get(f"{sut_url}/health")
print(response.status_code, response.text)
"""
# 本地图片路径和真实标签（根据实际情况修改）
image_path = "/path/to/your/test_image.jpg"
true_label = "cat" # 图片的真实标签
"""
"""
# 请求头
headers = {"Content-Type": "application/json"}
# 请求体（可指定限制处理的图片数量）
body = {"limit": 20 } # 可选参数，限制处理的图片总数
# 发送POST请求
response = requests.post(
f"{sut_url}/v1/private/s782b4996",
data=json.dumps(body),
headers=headers
)
"""
"""
# 读取图片文件
with open(image_path, 'rb') as f:
files = {'image': f}
# 发送POST请求
response = requests.post(f"{sut_url}/v1/private/s782b4996", files=files)
# 解析响应结果
if response.status_code == 200:
result = response.json()
print("预测评估结果:")
print(f"准确率: {result['metrics']['accuracy']}%")
print(f"平均召回率: {result['metrics']['average_recall']}%")
print(f"处理图片总数: {result['metrics']['total_images']}")
else:
print(f"请求失败，状态码: {response.status_code}")
print(f"错误信息: {response.text}")
"""
###############################################################################################
@@ -692,49 +566,7 @@ if __name__ == '__main__':
cpu_false_negatives = 0
cpu_total_processing_time = 0.0
"""
# 遍历所有类别文件夹
for folder_name in tqdm(os.listdir(dataset_root), desc="处理类别"):
folder_path = os.path.join(dataset_root, folder_name)
# 提取类别名（从"序号.name"格式中提取name部分）
class_name = folder_name.split('.', 1)[1].strip().lower()
# 获取文件夹中所有图片
image_files = []
for file in os.listdir(folder_path):
if file.lower().endswith(image_extensions):
image_files.append(os.path.join(folder_path, file))
# 随机抽取指定数量的图片（如果不足则取全部）
selected_images = random.sample(
image_files,
min(samples_per_class, len(image_files))
)
# 处理选中的图片
for img_path in selected_images:
total_count += 1
# 发送预测请求
prediction, error = test_image_prediction(sut_url, img_path)
if error:
print(f"处理图片 {img_path} 失败: {error}")
continue
# 解析预测结果
pred_class = prediction.get('class_name', '').lower()
confidence = prediction.get('confidence', 0)
# 判断是否预测正确（真实类别是否在预测类别中）
if class_name in pred_class:
correct_predictions += 1
# 可选：打印详细结果
print(f"图片: {os.path.basename(img_path)} | 真实: {class_name} | 预测: {pred_class} | 置信度: {confidence:.4f} | {'正确' if is_correct else '错误'}")
"""
# 遍历所有类别文件夹
for folder_name in os.listdir(dataset_root):
@@ -815,25 +647,7 @@ if __name__ == '__main__':
print(f"GPU预测: {gpu_pred_class} | {'正确' if gpu_is_correct else '错误'} | 耗时: {gpu_processing_time:.6f}s")
print(f"CPU预测: {cpu_pred_class} | {'正确' if cpu_is_correct else '错误'} | 耗时: {cpu_processing_time:.6f}s")
print("-" * 50)
"""
# 计算整体指标（在单标签场景下，准确率=召回率）
if total_samples == 0:
overall_accuracy = 0.0
overall_recall = 0.0
else:
overall_accuracy = correct_predictions / total_samples
overall_recall = correct_predictions / total_samples # 整体召回率
# 输出统计结果
print("\n" + "="*50)
print(f"测试总结:")
print(f"总测试样本数: {total_samples}")
print(f"正确预测样本数: {correct_predictions}")
print(f"整体准确率: {overall_accuracy:.4f} ({correct_predictions}/{total_samples})")
print(f"整体召回率: {overall_recall:.4f} ({correct_predictions}/{total_samples})")
print("="*50)
"""
# 初始化结果字典
result = {
@@ -899,25 +713,7 @@ if __name__ == '__main__':
log.error(f"gpu与cpu准确率差别超过3%，模型结果不正确")
change_product_unavailable()
"""
if result['accuracy_1_1'] < 0.9:
log.error(f"1:1正确率未达到90%, 视为产品不可用")
change_product_unavailable()
if result['accuracy_1_N'] < 1:
log.error(f"1:N正确率未达到100%, 视为产品不可用")
change_product_unavailable()
if result['1_1_latency'] > 0.5:
log.error(f"1:1平均latency超过0.5s, 视为产品不可用")
change_product_unavailable()
if result['1_N_latency'] > 0.5:
log.error(f"1:N平均latency超过0.5s, 视为产品不可用")
change_product_unavailable()
if result['enroll_latency'] > 1:
log.error(f"enroll(入库)平均latency超过1s, 视为产品不可用")
change_product_unavailable()
"""
exit_code = 0