python小练习：涉及print，json，numpy

枚举参考文件夹中的文件，并与待比较文件件中的同名文件比较是否一致。

#! /usr/bin/python3.6
# -*- coding:utf-8 -*-

import os
import sys
import json
import numpy as np
from sqlalchemy import false


def cmp_file(ref_file: str, dst_file: str) -> bool:
    ref_base_name = os.path.basename(ref_file)
    dst_base_name = os.path.basename(dst_file)
    assert os.path.exists(ref_file), f"ref file not exist: {ref_base_name}"
    if not os.path.exists(dst_file):
        print(f'dst file not exist: {dst_base_name}')
        return false

    ref_data = np.fromfile(ref_file, dtype=np.ubyte, count=-1)
    dst_data = np.fromfile(dst_file, dtype=np.ubyte, count=-1)
    is_equal = np.array_equal(ref_data, dst_data)
    print(is_equal, ": ", ref_base_name)
    return is_equal


def cmp_dir(ref_dir: str, dst_dir: str) -> None:
    print(f'\n==========>>> Start compare {ref_dir} and {dst_dir}')
    ref_names = os.listdir(ref_dir)
    for name in ref_names:
        ref_file = os.path.join(ref_dir, name)
        dst_file = os.path.join(dst_dir, name)
        cmp_file(ref_file, dst_file)


def main():
    if len(sys.argv) < 2:
        print('usage: dump_dir_cmp.py dir_config.json')
        return

    json_file = sys.argv[1]
    with open(json_file) as fp:
        js_data = json.load(fp)
        for dst_dir in js_data['dst_dirs']:
            cmp_dir(js_data['ref_dir'], dst_dir)


if (__name__ == '__main__'):
    main()

配置样例：

{
    "ref_dir": "./dump_data/NPU_DUMPF001_P0/tensorflow_squeezenet_task0_loop0",
    "dst_dirs": [
        "./dump_data/NPU_DUMPF002_P0/tensorflow_squeezenet_task0_loop0",
        "./dump_data/NPU_DUMPF002_P0/tensorflow_squeezenet_task1_loop0",
        "./dump_data/NPU_DUMPF002_P0/tensorflow_squeezenet_task2_loop0",
        "./dump_data/NPU_DUMPF002_P0/tensorflow_squeezenet_task3_loop0",
        "./dump_data/NPU_DUMPF002_P0/tensorflow_squeezenet_task4_loop0",
        "./dump_data/NPU_DUMPF002_P0/tensorflow_squeezenet_task5_loop0"
    ]
}

样例2（re匹配）：

#! /usr/bin/python3.6
# -*- coding:utf-8 -*-
# cmp_dump_pickle_dir.py

import os
import re
import sys
import numpy as np
from numpy.linalg import norm
import pickle
import shutil
from sklearn.metrics.pairwise import cosine_similarity


def vec_similarity(v1: np.array, v2: np.array):
    sim = cosine_similarity(v1.reshape(1, v1.size), v2.reshape(1, v2.size))
    return sim[0][0]
    # norm2 = norm(v1) * norm(v2)
    # cosine = np.dot(v1,v2) / norm2
    # return cosine


def re_find_file(dir: str, op_name: str) -> str:
    for fname in os.listdir(dir): # 分组匹配: (...|...)
        re_dst = re.search(f"{op_name}_(out_[\S]*|out\d).bin$", fname)
        if re_dst is not None:
            return re_dst.group()
    return None


def cmp_file(ref_file: str, dst_file: str, dtype: str) -> bool:
    ref_base_name = os.path.basename(ref_file)
    dst_base_name = os.path.basename(dst_file)
    assert os.path.exists(ref_file), f"ref file not exist: {ref_base_name}"
    assert os.path.exists(dst_file), f"dst file not exist: {dst_base_name}"

    ref_data = np.fromfile(ref_file, dtype=dtype, count=-1)
    dst_data = np.fromfile(dst_file, dtype=dtype, count=-1)
    if dtype == 'float32' or dtype == 'float16':
        sim = vec_similarity(ref_data, dst_data)
        print(sim > 0.95, f", simularity={sim} : ", ref_base_name)
        return (sim > 0.95)

    is_equal = np.array_equal(ref_data, dst_data)
    print(is_equal, ": ", ref_base_name)
    return is_equal


def cmp_dir(ref_dir: str, dst_dir: str) -> None:
    print(f'\n==========>>> Start compare {ref_dir} and {dst_dir}')
    patten = re.compile(r"_op_out_[\S]*.bin$")
    ref_names = os.listdir(ref_dir)
    not_exist_ops = []
    for ref_name in ref_names:
        assert re.match(r"[\S]*_op_out_[\S]*.bin$", ref_name) is not None, f"bad file name: {ref_name}"
        dtype = ref_name[ref_name.rfind('_') + 1:ref_name.rfind('.')]
        mdl_name = ref_name[0:patten.search(ref_name).span()[0]]
        dst_name = re_find_file(dst_dir, mdl_name)
        if dst_name is None:
            not_exist_ops.append(mdl_name)
            continue

        ref_file = os.path.join(ref_dir, ref_name)
        dst_file = os.path.join(dst_dir, dst_name)
        cmp_file(ref_file, dst_file, dtype=dtype)

    print(f'\nNot exist ops: {not_exist_ops}')


def dump_pickle_file(pickle_file: str, out_bin_dir: str, force_dtype_u8: bool) -> None:
    def is_float_type(data_buff: np.ndarray) -> bool:
        return data_buff.dtype == np.float16 or data_buff.dtype == np.float32

    with open(pickle_file, "rb") as f:
        op_ref = pickle.load(f)
        for i, (key, value) in enumerate(op_ref.items()):
            data_buff = value.flatten()
            # print("layer: ", key, " shape: ", value.shape, " type: ", value.dtype, " size: ", value.size)
            dtype = 'uint8' if force_dtype_u8 and is_float_type(data_buff) else data_buff.dtype
            print("pickle key: %30s, size: %7d, dtype: %s" % (key, value.itemsize * value.size, data_buff.dtype))
            data_buff.tofile(os.path.join(out_bin_dir, key.replace("/", "_") + f"_op_out_{dtype}.bin"))

    #print("op ref: type ", type(op_ref), op_ref.size)
    #print("op shape: type ", op_ref['data'].shape)


def mkdir(dir: str) -> None:
    if os.path.exists(dir):
        shutil.rmtree(dir)
    os.mkdir(dir)


def main():
    assert len(sys.argv) >= 4, 'usage: dump_dir_cmp.py pickle_file pickle_out_dir dst_dump_dir [force_dtype_u8]'
    force_dtype_u8 = True if len(sys.argv) >= 5 and sys.argv[4] == 'force_dtype_u8' else False

    #np.seterr('raise')
    mkdir(sys.argv[2])
    dump_pickle_file(sys.argv[1], sys.argv[2], force_dtype_u8)
    cmp_dir(sys.argv[2], sys.argv[3])


if (__name__ == '__main__'):
    main()