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• Dispersion-based-Clustering-master
  • run.py
  • LICENSE
  • run.sh
  • README.md
  • reid
    • datasets
      • __pycache__
        • __init__.cpython-36.pyc
        • __init__.cpython-37.pyc
        • mars.cpython-37.pyc
        • dukemtmc_videoReID.cpython-36.pyc
        • market1501.cpython-36.pyc
        • mars.cpython-36.pyc
        • duke.cpython-36.pyc
      • mars.py
      • dukemtmc_videoReID.py
      • __init__.py
      • market1501.py
      • duke.py
    • utils
      • meters.py
      • __pycache__
        • __init__.cpython-36.pyc
        • serialization.cpython-36.pyc
        • __init__.cpython-37.pyc
        • meters.cpython-36.pyc
        • osutils.cpython-36.pyc
        • logging.cpython-36.pyc
      • osutils.py
      • bottom_up.py
      • serialization.py
      • data
        • sampler.py
        • __pycache__
          • preprocessor.cpython-36.pyc
          • dataset.cpython-36.pyc
          • transforms.cpython-36.pyc
          • __init__.cpython-36.pyc
        • preprocessor.py
        • __init__.py
        • dataset.py
        • transforms.py
      • __init__.py
      • logging.py
    • evaluation_metrics
      • ranking.py
      • __pycache__
        • ranking.cpython-36.pyc
        • __init__.cpython-36.pyc
        • classification.cpython-36.pyc
      • classification.py
      • __init__.py
    • evaluators.py
    • trainers.py
    • models
      • __pycache__
        • end2end.cpython-36.pyc
        • resnet.cpython-36.pyc
        • __init__.cpython-36.pyc
      • resnet.py
      • end2end.py
      • __init__.py
    • dist_metric.py
    • feature_extraction
      • __pycache__
        • cnn.cpython-36.pyc
        • __init__.cpython-36.pyc
      • cnn.py
      • __init__.py
    • bottom_up.py
    • __init__.py
    • exclusive_loss.py
    • metric_learning
      • __pycache__
        • kissme.cpython-36.pyc
        • __init__.cpython-36.pyc
        • euclidean.cpython-36.pyc
      • kissme.py
      • euclidean.py
      • __init__.py

from __future__ import print_function, absolute_import
import json
import os.path as osp
import shutil

import torch
from torch.nn import Parameter

from .osutils import mkdir_if_missing


def read_json(fpath):
    with open(fpath, 'r') as f:
        obj = json.load(f)
    return obj


def write_json(obj, fpath):
    mkdir_if_missing(osp.dirname(fpath))
    with open(fpath, 'w') as f:
        json.dump(obj, f, indent=4, separators=(',', ': '))


def save_checkpoint(state, is_best, fpath='checkpoint.pth.tar'):
    mkdir_if_missing(osp.dirname(fpath))
    torch.save(state, fpath)
    if is_best:
        shutil.copy(fpath, osp.join(osp.dirname(fpath), 'model_best.pth.tar'))


def load_checkpoint(fpath):
    if osp.isfile(fpath):
        checkpoint = torch.load(fpath)
        print("=> Loaded checkpoint '{}'".format(fpath))
        return checkpoint
    else:
        raise ValueError("=> No checkpoint found at '{}'".format(fpath))


def copy_state_dict(state_dict, model, strip=None):
    tgt_state = model.state_dict()
    copied_names = set()
    for name, param in state_dict.items():
        if strip is not None and name.startswith(strip):
            name = name[len(strip):]
        if name not in tgt_state:
            continue
        if isinstance(param, Parameter):
            param = param.data
        if param.size() != tgt_state[name].size():
            print('mismatch:', name, param.size(), tgt_state[name].size())
            continue
        tgt_state[name].copy_(param)
        copied_names.add(name)

    missing = set(tgt_state.keys()) - copied_names
    if len(missing) > 0:
        print("missing keys in state_dict:", missing)

    return model