add parse_profile

python/examples/bert/parse_profile.py

 import sys 
 import os
-## general info
-cuda_version = "10.1"
-cudnn_version = "7"
-trt_version = "6"
-python_version = "3.6"
-gcc_version = "8.2"
-paddle_version = "2.0.0"
-cpu = "Xeon 6148"
-gpu = "P4"
-xpu = ""
-api = ""
-owner = ""
-## model info
-model_name = "bert_seq128"
-model_type = "static"
-model_source = "paddlehub"
-model_url = ""
-## data info
-batch_size = "1"
-num_of_samples = "1"
-input_shape = "none"
-## conf info
-runtime_device = "cpu"
-ir_optim = "true"
-enable_memory_optim = "true"
-enable_tensorrt = "false"
-precision = "fp32"
-enable_mkldnn = "false"
-cpu_math_library_num_threads = "1"
-## acc info 
-acc1 = ""
-acc5 = ""
-## perf info
-average_latency, QPS = "", ""
-process_latency = ""
-cpu_rss, vms, shared, dirty, cpu_usage = "", "", "", "", ""
-gpu_id, total, free, used, gpu_utilization_rate, gpu_mem_utilization_rate = "","","","","", ""
+import yaml
 
 class LogHandler(object):
     def __init__(self):
@@ -52,43 +16,85 @@ class LogHandler(object):
     def append(self, new_str):
         self.fstr += new_str + "\n"
 
-fh = LogHandler()
+if __name__ == "__main__":
+    filename = sys.argv[1]
+    f = open(filename, 'r')
+    config = yaml.load(f)
+    ## general info
+    cuda_version = config["cuda_version"]
+    cudnn_version = config["cudnn_version"]
+    trt_version = config["cudnn_version"]
+    python_version = config["python_version"]
+    gcc_version = config["gcc_version"]
+    paddle_version = config["paddle_servion"]
+    cpu = config["cpu"]
+    gpu = config["gpu"]
+    xpu = config["xpu"]
+    api = config["api"]
+    owner = config["owner"]
+    ## model info
+    model_name = config["model_name"]
+    model_type = config["model_type"]
+    model_source = config["model_source"]
+    model_url = config["model_url"]
+    ## data info
+    batch_size = config["batch_size"]
+    num_of_samples = config["num_of_samples"]
+    input_shape = config["input_shape"]
+    ## conf info
+    runtime_device = config["runtime_device"]
+    ir_optim = config["ir_optim"]
+    enable_memory_optim = config["enable_memory_optim"]
+    enable_tensorrt = config["enable_tensorrt"]
+    precision = config["precision"]
+    enable_mkldnn = config["enable_mkldnn"]
+    cpu_math_library_num_threads = config["cpu_math_library_num_threads"]
+    ## acc info 
+    acc1 = "Nan"
+    acc5 = "Nan"
+    ## perf info
+    average_latency, QPS = "", ""
+    process_latency = ""
+    cpu_rss, vms, shared, dirty, cpu_usage = "", "", "", "", ""
+    gpu_id, total, free, used, gpu_utilization_rate, gpu_mem_utilization_rate = "","","","","", ""
 
-fh.append("cuda_version: {}".format(cuda_version))
-fh.append("cudnn_version: {}".format(cudnn_version))
-fh.append("trt_version: {} ".format(trt_version))
-fh.append("python_version: {}".format(python_version))
-fh.append("gcc_version: {}".format(gcc_version))
-fh.append("paddle_version: {}".format(paddle_version))
-fh.append("cpu: {}".format(cpu))
-fh.append("gpu: {}".format(gpu)) # p4, v100, 1080
-fh.append("xpu: {}".format(xpu)) 
-fh.append("api: {}".format(api))
-fh.append("owner: {}".format(owner))
-fh.append("----------------------- Model info ----------------------")
-fh.append("model_name: {}".format(model_name))
-fh.append("model_type: {}".format(model_type))
-fh.append("model_source: {}".format(model_source))
-fh.append("model_url: {}".format(model_url))
-fh.append("----------------------- Data info -----------------------")
-fh.append("batch_size: {}".format(batch_size))
-fh.append("num_of_samples: {}".format(num_of_samples))
-fh.append("input_shape: {}".format(input_shape))
-fh.append("----------------------- Conf info -----------------------")
-fh.append("runtime_device: {}".format(runtime_device))
-fh.append("ir_optim: {}".format(ir_optim))
-fh.append("enable_memory_optim: {}".format(enable_memory_optim))
-fh.append("enable_tensorrt: {}".format(enable_tensorrt))
-fh.append("precision: {}".format(precision))    # fp32, fp16, int8
-fh.append("enable_mkldnn: {}".format(enable_mkldnn))
-fh.append("cpu_math_library_num_threads: {}".format(cpu_math_library_num_threads))
-fh.append("----------------------- Acc info ------------------------")
-fh.append("acc1:".format(acc1))
-fh.append("acc5:".format(acc5))
-fh.append("----------------------- Perf info -----------------------")
-fh.append("average_latency(ms): {}, QPS: {}".format(average_latency, QPS))
-fh.append("process_latency(ms): {}".format(process_latency))
-fh.append("process_name: clas_benchmark, cpu_rss(MB): {}, vms(MB): {}, shared(MB): {}, dirty(MB): {}, cpu_usage(%): {}".format(cpu_rss, vms, shared, dirty, cpu_usage))
-fh.append("gpu_id: {}, total(MB): {}, free(MB): {}, used(MB): {}, gpu_utilization_rate(%): {}, gpu_mem_utilization_rate(%): {}".format(gpu_id, total, free, used, gpu_utilization_rate, gpu_mem_utilization_rate))
+    fh = LogHandler()
+
+    fh.append("cuda_version: {}".format(cuda_version))
+    fh.append("cudnn_version: {}".format(cudnn_version))
+    fh.append("trt_version: {} ".format(trt_version))
+    fh.append("python_version: {}".format(python_version))
+    fh.append("gcc_version: {}".format(gcc_version))
+    fh.append("paddle_version: {}".format(paddle_version))
+    fh.append("cpu: {}".format(cpu))
+    fh.append("gpu: {}".format(gpu)) # p4, v100, 1080
+    fh.append("xpu: {}".format(xpu)) 
+    fh.append("api: {}".format(api))
+    fh.append("owner: {}".format(owner))
+    fh.append("----------------------- Model info ----------------------")
+    fh.append("model_name: {}".format(model_name))
+    fh.append("model_type: {}".format(model_type))
+    fh.append("model_source: {}".format(model_source))
+    fh.append("model_url: {}".format(model_url))
+    fh.append("----------------------- Data info -----------------------")
+    fh.append("batch_size: {}".format(batch_size))
+    fh.append("num_of_samples: {}".format(num_of_samples))
+    fh.append("input_shape: {}".format(input_shape))
+    fh.append("----------------------- Conf info -----------------------")
+    fh.append("runtime_device: {}".format(runtime_device))
+    fh.append("ir_optim: {}".format(ir_optim))
+    fh.append("enable_memory_optim: {}".format(enable_memory_optim))
+    fh.append("enable_tensorrt: {}".format(enable_tensorrt))
+    fh.append("precision: {}".format(precision))    # fp32, fp16, int8
+    fh.append("enable_mkldnn: {}".format(enable_mkldnn))
+    fh.append("cpu_math_library_num_threads: {}".format(cpu_math_library_num_threads))
+    fh.append("----------------------- Acc info ------------------------")
+    fh.append("acc1:".format(acc1))
+    fh.append("acc5:".format(acc5))
+    fh.append("----------------------- Perf info -----------------------")
+    fh.append("average_latency(ms): {}, QPS: {}".format(average_latency, QPS))
+    fh.append("process_latency(ms): {}".format(process_latency))
+    fh.append("process_name: clas_benchmark, cpu_rss(MB): {}, vms(MB): {}, shared(MB): {}, dirty(MB): {}, cpu_usage(%): {}".format(cpu_rss, vms, shared, dirty, cpu_usage))
+    fh.append("gpu_id: {}, total(MB): {}, free(MB): {}, used(MB): {}, gpu_utilization_rate(%): {}, gpu_mem_utilization_rate(%): {}".format(gpu_id, total, free, used, gpu_utilization_rate, gpu_mem_utilization_rate))
 
 fh.dump()