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gen_eval_table.py 3.47 KB
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import sys
import os
import json

rootdir = os.path.abspath(sys.path[0] + '/../')

datadir = f"{rootdir}/data/__new/classroom_fovea_r360x80_t0.6"
n_nets_arr = [ 1, 2, 4, 8 ]
nf_arr = [ 64, 128, 256, 512, 1024 ]
n_layers_arr = [ 2, 4, 8 ]

head = "Nets,Layers," + ",".join([f"{val}" for val in nf_arr])
perf_train_table = []
perf_test_table = []
perf_time_table = []
for n_nets in n_nets_arr:
    for n_layers in n_layers_arr:
        perf_train_row = []
        perf_test_row = []
        perf_time_row = []
        for nf in nf_arr:
            configid = f"eval@snerffast{n_nets}-rgb_e6_fc{nf}x{n_layers}_d1.00-7.00_s64_~p"
            outputdir = f"{datadir}/{configid}/output_50"
            if not os.path.exists(outputdir):
                perf_train_row.append("-")
                perf_test_row.append("-")
                perf_time_row.append("-")
                continue
            perf_test_found=False
            perf_train_found=False
            for file in os.listdir(outputdir):
                if file.startswith("perf_r120x80_test"):
                    if perf_test_found:
                        os.remove(f"{outputdir}/{file}")
                    else:
                        perf_test_row.append(os.path.splitext(file)[0].split("_")[-1])
                        perf_test_found=True
                elif file.startswith("perf_r120x80"):
                    if perf_train_found:
                        os.remove(f"{outputdir}/{file}")
                    else:
                        perf_train_row.append(os.path.splitext(file)[0].split("_")[-1])
                        perf_train_found=True
            if perf_train_found == False:
                perf_train_row.append("-")
            if perf_test_found == False:
                perf_test_row.append("-")
            # Collect time values
            time_file = f"{datadir}/eval_trt/time/eval_{n_nets}x{nf}x{n_layers}.json"
            if not os.path.exists(time_file):
                perf_time_row.append("-")
            else:
                with open(time_file) as fp:
                    time_data = json.load(fp)
                time = 0
                for item in time_data:
                    time += item['computeMs']
                time /= len(time_data)
                perf_time_row.append(f"{time:.1f}")
        perf_train_table.append(perf_train_row)
        perf_test_table.append(perf_test_row)
        perf_time_table.append(perf_time_row)

perf_train_content = head + "\n"
for i, row in enumerate(perf_train_table):
    if i % len(n_layers_arr) == 0:
        perf_train_content += f"{n_nets_arr[i // len(n_layers_arr)]}"
    perf_train_content += f",{n_layers_arr[i % len(n_layers_arr)]},"
    perf_train_content += ",".join(row) + "\n"

perf_test_content = head + "\n"
for i, row in enumerate(perf_test_table):
    if i % len(n_layers_arr) == 0:
        perf_test_content += f"{n_nets_arr[i // len(n_layers_arr)]}"
    perf_test_content += f",{n_layers_arr[i % len(n_layers_arr)]},"
    perf_test_content += ",".join(row) + "\n"

perf_time_content = head + "\n"
for i, row in enumerate(perf_time_table):
    if i % len(n_layers_arr) == 0:
        perf_time_content += f"{n_nets_arr[i // len(n_layers_arr)]}"
    perf_time_content += f",{n_layers_arr[i % len(n_layers_arr)]},"
    perf_time_content += ",".join(row) + "\n"

with open(f"{datadir}/eval_perf.csv", "w") as fp:
    fp.write("Train:\n")
    fp.write(perf_train_content)
    fp.write("Test:\n")
    fp.write(perf_test_content)
    fp.write("Time:\n")
    fp.write(perf_time_content)