HandRefinerPortable/mesh_graphormer/utils/miscellaneous.py

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import errno
import os
import os.path as op
import re
import logging
import numpy as np
import torch
import random
import shutil
from .comm import is_main_process
import yaml


def mkdir(path):
    # if it is the current folder, skip.
    # otherwise the original code will raise FileNotFoundError
    if path == '':
        return
    try:
        os.makedirs(path)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise


def save_config(cfg, path):
    if is_main_process():
        with open(path, 'w') as f:
            f.write(cfg.dump())


def config_iteration(output_dir, max_iter):
    save_file = os.path.join(output_dir, 'last_checkpoint')
    iteration = -1
    if os.path.exists(save_file):
        with open(save_file, 'r') as f:
            fname = f.read().strip()
        model_name = os.path.basename(fname)
        model_path = os.path.dirname(fname)
        if model_name.startswith('model_') and len(model_name) == 17:
            iteration = int(model_name[-11:-4])
        elif model_name == "model_final":
            iteration = max_iter
        elif model_path.startswith('checkpoint-') and len(model_path) == 18:
            iteration = int(model_path.split('-')[-1])
    return iteration


def get_matching_parameters(model, regexp, none_on_empty=True):
    """Returns parameters matching regular expression"""
    if not regexp:
        if none_on_empty:
            return {}
        else:
            return dict(model.named_parameters())
    compiled_pattern = re.compile(regexp)
    params = {}
    for weight_name, weight in model.named_parameters():
        if compiled_pattern.match(weight_name):
            params[weight_name] = weight
    return params


def freeze_weights(model, regexp):
    """Freeze weights based on regular expression."""
    logger = logging.getLogger("maskrcnn_benchmark.trainer")
    for weight_name, weight in get_matching_parameters(model, regexp).items():
        weight.requires_grad = False
        logger.info("Disabled training of {}".format(weight_name))


def unfreeze_weights(model, regexp, backbone_freeze_at=-1,
        is_distributed=False):
    """Unfreeze weights based on regular expression.
    This is helpful during training to unfreeze freezed weights after
    other unfreezed weights have been trained for some iterations.
    """
    logger = logging.getLogger("maskrcnn_benchmark.trainer")
    for weight_name, weight in get_matching_parameters(model, regexp).items():
        weight.requires_grad = True
        logger.info("Enabled training of {}".format(weight_name))
    if backbone_freeze_at >= 0:
        logger.info("Freeze backbone at stage: {}".format(backbone_freeze_at))
        if is_distributed:
            model.module.backbone.body._freeze_backbone(backbone_freeze_at)
        else:
            model.backbone.body._freeze_backbone(backbone_freeze_at)


def delete_tsv_files(tsvs):
    for t in tsvs:
        if op.isfile(t):
            try_delete(t)
        line = op.splitext(t)[0] + '.lineidx'
        if op.isfile(line):
            try_delete(line)


def concat_files(ins, out):
    mkdir(op.dirname(out))
    out_tmp = out + '.tmp'
    with open(out_tmp, 'wb') as fp_out:
        for i, f in enumerate(ins):
            logging.info('concating {}/{} - {}'.format(i, len(ins), f))
            with open(f, 'rb') as fp_in:
                shutil.copyfileobj(fp_in, fp_out, 1024*1024*10)
    os.rename(out_tmp, out)


def concat_tsv_files(tsvs, out_tsv):
    concat_files(tsvs, out_tsv)
    sizes = [os.stat(t).st_size for t in tsvs]
    sizes = np.cumsum(sizes)
    all_idx = []
    for i, t in enumerate(tsvs):
        for idx in load_list_file(op.splitext(t)[0] + '.lineidx'):
            if i == 0:
                all_idx.append(idx)
            else:
                all_idx.append(str(int(idx) + sizes[i - 1]))
    with open(op.splitext(out_tsv)[0] + '.lineidx', 'w') as f:
        f.write('\n'.join(all_idx))


def load_list_file(fname):
    with open(fname, 'r') as fp:
        lines = fp.readlines()
    result = [line.strip() for line in lines]
    if len(result) > 0 and result[-1] == '':
        result = result[:-1]
    return result


def try_once(func):
    def func_wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logging.info('ignore error \n{}'.format(str(e)))
    return func_wrapper


@try_once
def try_delete(f):
    os.remove(f)


def set_seed(seed, n_gpu):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(seed)


def print_and_run_cmd(cmd):
    print(cmd)
    os.system(cmd)


def write_to_yaml_file(context, file_name):
    with open(file_name, 'w') as fp:
        yaml.dump(context, fp, encoding='utf-8')


def load_from_yaml_file(yaml_file):
    with open(yaml_file, 'r') as fp:
        return yaml.load(fp, Loader=yaml.CLoader)