133 lines
4.1 KiB
Python
Executable File
133 lines
4.1 KiB
Python
Executable File
from mylog.mylog import MyLogger
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from op_generator import op_generator
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from cost_test_conf import Config
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import subprocess as sp
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import os
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from lmfit import Model
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import numpy as np
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# step 1 gen op and conf
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material_cls = op_generator.gen_operator("material")
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conf = Config()
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conf.u_to_test_op_c = 'material'
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conf.is_not_running_as_unittest_c = True
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conf.schema_file_c = 'c10k1.schema'
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conf.row_count_c = 1000
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conf.input_projector_count_c = 1
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material_op = material_cls(conf)
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result_file_name = 'material_result'
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if os.path.exists(result_file_name):
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os.remove(result_file_name)
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# step 2 do_bench and gen data
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row_count_max = 1001
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row_count_step = 100
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column_counts = [3, 5, 8]
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case_run_time = 7
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total_case_count = (row_count_max / row_count_step + 1) * len(column_counts) * case_run_time
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case_count = 0
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print "Total case count %s ..." % (total_case_count)
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for row_count in xrange(1, row_count_max + 1, row_count_step):
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for column_count in column_counts:
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for time in xrange(case_run_time):
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case_count += 1
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material_op.conf.row_count_c = row_count
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material_op.conf.input_projector_count_c = column_count
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sp.check_call("echo -n '%s,' >> %s" % (row_count, result_file_name), shell=True)
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sp.check_call("echo -n '%s,' >> %s" % (column_count, result_file_name), shell=True)
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print "Running case %s / %s ... : %s " % (case_count, total_case_count, material_op.get_bench_cmd())
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print "%s >> %s" % (material_op.get_bench_cmd(), result_file_name)
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sp.check_call("%s >> %s" % (material_op.get_bench_cmd(), result_file_name), shell=True)
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# step 3 preprocess data
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final_file_name = "material_result_final"
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if os.path.exists("material_final_result"):
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os.remove("material_final_result")
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data_cmd = material_op.get_data_preprocess_cmd()
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sp.check_call(data_cmd, shell=True)
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# step 4 fit and output
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# given model form, do fit using previous result data
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# case param should be considered with cost_model_util.cpp output format
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# eg: material_test() in cost_model_util.cpp
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# output row_count, cost_time
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out_model_file_name = "material_model"
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if os.path.exists(out_model_file_name):
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os.remove(out_model_file_name)
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def material_model_form(args,
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# Tstartup,
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Trow_once,
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Trow_col):
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(
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Nrow,
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Ncol,
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) = args
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total_cost = 0 # Tstartup
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total_cost += Nrow * (Trow_once + Ncol * Trow_col)
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return total_cost
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def material_model_arr(arg_sets,
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# Tstartup,
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Trow_once,
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Trow_col):
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res = []
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for single_arg_set in arg_sets:
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res.append(material_model_form(single_arg_set,
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# Tstartup,
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Trow_once,
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Trow_col))
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return np.array(res)
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def extract_info_from_line(line):
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splited = line.split(",")
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line_info = []
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for item in splited:
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line_info.append(float(item))
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return line_info
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material_model = Model(material_model_arr)
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material_model.set_param_hint("Trow_once", min=0.0)
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material_model.set_param_hint("Trow_col", min=0.0)
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file = open(final_file_name, "r")
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arg_sets = []
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times = []
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case_params = []
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for line in file:
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if line.startswith('#'):
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continue
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case_param = extract_info_from_line(line)
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case_params.append(case_param)
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arg_sets.append((case_param[0], case_param[1]))
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times.append(case_param[3])
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file.close()
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arg_sets_np = np.array(arg_sets)
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times_np = np.array(times)
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# result is the fitting result model
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result = material_model.fit(times_np, arg_sets=arg_sets_np,
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# Tstartup=10.0,
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Trow_once=10.0,
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Trow_col=1.0
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)
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# res_line = str(result.best_values["Tstartup"]) + ","
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res_line = str(result.best_values["Trow_once"]) + ","
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res_line += str(result.best_values["Trow_col"])
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print result.fit_report()
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if out_model_file_name:
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out_file = open(out_model_file_name, "w")
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out_file.write(res_line)
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out_file.close()
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