Python os.makedirs方法代码示例

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def downloadDemo(which):
    try:
        downloadDir = tempfile.mkdtemp()
        archivePath = "{}/svviz-data.zip".format(downloadDir)

        # logging.info("Downloading...")
        downloadWithProgress("http://svviz.github.io/svviz/assets/examples/{}.zip".format(which), archivePath)
        
        logging.info("Decompressing...")
        archive = zipfile.ZipFile(archivePath)
        archive.extractall("{}".format(downloadDir))

        if not os.path.exists("svviz-examples"):
            os.makedirs("svviz-examples/")

        shutil.move("{temp}/{which}".format(temp=downloadDir, which=which), "svviz-examples/")
    except Exception as e:
        print("error downloading and decompressing example data: {}".format(e))
        return False

    if not os.path.exists("svviz-examples"):
        print("error finding example data after download and decompression")
        return False
    return True 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def create_oa_folders(cls, type, date):

        # create date and ingest summary folder structure if they don't' exist.
        root_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
        data_type_folder = "{0}/data/{1}/{2}"
        if not os.path.isdir(data_type_folder.format(root_path, type, date)): os.makedirs(
            data_type_folder.format(root_path, type, date))
        if not os.path.isdir(data_type_folder.format(root_path, type, "ingest_summary")): os.makedirs(
            data_type_folder.format(root_path, type, "ingest_summary"))

        # create ipynb folders.
        ipynb_folder = "{0}/ipynb/{1}/{2}".format(root_path, type, date)
        if not os.path.isdir(ipynb_folder): os.makedirs(ipynb_folder)

        # retun path to folders.
        data_path = data_type_folder.format(root_path, type, date)
        ingest_path = data_type_folder.format(root_path, type, "ingest_summary")
        return data_path, ingest_path, ipynb_folder 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def ensure_session_manager_plugin():
    session_manager_dir = os.path.join(config.user_config_dir, "bin")
    PATH = os.environ.get("PATH", "") + ":" + session_manager_dir
    if shutil.which("session-manager-plugin", path=PATH):
        subprocess.check_call(["session-manager-plugin"], env=dict(os.environ, PATH=PATH))
    else:
        os.makedirs(session_manager_dir, exist_ok=True)
        target_path = os.path.join(session_manager_dir, "session-manager-plugin")
        if platform.system() == "Darwin":
            download_session_manager_plugin_macos(target_path=target_path)
        elif platform.linux_distribution()[0] == "Ubuntu":
            download_session_manager_plugin_linux(target_path=target_path)
        else:
            download_session_manager_plugin_linux(target_path=target_path, pkg_format="rpm")
        os.chmod(target_path, stat.S_IRUSR | stat.S_IWUSR | stat.S_IXUSR)
        subprocess.check_call(["session-manager-plugin"], env=dict(os.environ, PATH=PATH))
    return shutil.which("session-manager-plugin", path=PATH) 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def savepb(self):
		"""
		Create a standalone const graph def that 
		C++	can load and run.
		"""
		darknet_pb = self.to_darknet()
		flags_pb = self.FLAGS
		flags_pb.verbalise = False
		
		flags_pb.train = False
		# rebuild another tfnet. all const.
		tfnet_pb = TFNet(flags_pb, darknet_pb)		
		tfnet_pb.sess = tf.Session(graph = tfnet_pb.graph)
		# tfnet_pb.predict() # uncomment for unit testing
		name = 'built_graph/{}.pb'.format(self.meta['name'])
		os.makedirs(os.path.dirname(name), exist_ok=True)
		#Save dump of everything in meta
		with open('built_graph/{}.meta'.format(self.meta['name']), 'w') as fp:
			json.dump(self.meta, fp)
		self.say('Saving const graph def to {}'.format(name))
		graph_def = tfnet_pb.sess.graph_def
		tf.train.write_graph(graph_def,'./', name, False) 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def fetch_attacks_data(self):
    """Initializes data necessary to execute attacks.

    This method could be called multiple times, only first call does
    initialization, subsequent calls are noop.
    """
    if self.attacks_data_initialized:
      return
    # init data from datastore
    self.submissions.init_from_datastore()
    self.dataset_batches.init_from_datastore()
    self.adv_batches.init_from_datastore()
    # copy dataset locally
    if not os.path.exists(LOCAL_DATASET_DIR):
      os.makedirs(LOCAL_DATASET_DIR)
    eval_lib.download_dataset(self.storage_client, self.dataset_batches,
                              LOCAL_DATASET_DIR,
                              os.path.join(LOCAL_DATASET_COPY,
                                           self.dataset_name, 'images'))
    # download dataset metadata
    self.read_dataset_metadata()
    # mark as initialized
    self.attacks_data_initialized = True 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def writeDir_r(self, det_dir, dire, pp, r, all_type):
        #gen.log "writeDir_r:(%s)"%(det_dir)
        dirs = self.readDirItems(dire.locExtent, dire.lenData)
        for d in dirs:
            if not d.fIdentifier in [".", ".."]:
                if (pp != None) and (pp.search(d.fIdentifier) == None):
                    match = False
                else:
                    match = True
                #gen.log "mathing %s, %s, (%x)"%(match, d.fIdentifier, d.fFlag)
                p = det_dir + "/" + d.fIdentifier
                if d.fFlag & 0x02 == 0x02:
                    if not os.path.exists(p):
                        os.makedirs(p, 0o777)
                    if r:
                        if match:
                            self.writeDir_r(p, d, None, r, all_type) # Don't need to match subdirectory.
                        else:
                            self.writeDir_r(p, d, pp, r, all_type)
                elif match:
                    self.writeFile(d, p, all_type)
            # if not d.fIdentifier end #
        # for d in dirs end # 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def _save_files(self, data, dtype_out_time):
        """Save the data to netcdf files in direc_out."""
        path = self.path_out[dtype_out_time]
        if not os.path.isdir(self.dir_out):
            os.makedirs(self.dir_out)
        if 'reg' in dtype_out_time:
            try:
                reg_data = xr.open_dataset(path)
            except (EOFError, RuntimeError, IOError):
                reg_data = xr.Dataset()
            reg_data.update(data)
            data_out = reg_data
        else:
            data_out = data
        if isinstance(data_out, xr.DataArray):
            data_out = xr.Dataset({self.name: data_out})
        data_out.to_netcdf(path, engine='netcdf4') 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def save_to_path(self, filepath):
        """Save retrieved data to file at ``filepath``.

        .. versionadded: 1.9.6

        :param filepath: Path to save retrieved data.

        """
        filepath = os.path.abspath(filepath)
        dirname = os.path.dirname(filepath)
        if not os.path.exists(dirname):
            os.makedirs(dirname)

        self.stream = True

        with open(filepath, 'wb') as fileobj:
            for data in self.iter_content():
                fileobj.write(data) 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def plot_examples(data_loader, model, epoch, plotter, ind = [0, 10, 20]):

    # switch to evaluate mode
    model.eval()

    for i, (g, h, e, target) in enumerate(data_loader):
        if i in ind:
            subfolder_path = 'batch_' + str(i) + '_t_' + str(int(target[0][0])) + '/epoch_' + str(epoch) + '/'
            if not os.path.isdir(args.plotPath + subfolder_path):
                os.makedirs(args.plotPath + subfolder_path)

            num_nodes = torch.sum(torch.sum(torch.abs(h[0, :, :]), 1) > 0)
            am = g[0, 0:num_nodes, 0:num_nodes].numpy()
            pos = h[0, 0:num_nodes, :].numpy()

            plotter.plot_graph(am, position=pos, fig_name=subfolder_path+str(i) + '_input.png')

            # Prepare input data
            if args.cuda:
                g, h, e, target = g.cuda(), h.cuda(), e.cuda(), target.cuda()
            g, h, e, target = Variable(g), Variable(h), Variable(e), Variable(target)

            # Compute output
            model(g, h, e, lambda cls, id: plotter.plot_graph(am, position=pos, cls=cls,
                                                          fig_name=subfolder_path+ id)) 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def get_models_overall(exp_name, rbp):
    print("RBP: " + rbp)

    out_h5 = "{rbp}/model_files/model.h5".format(rbp=rbp)
    os.makedirs(os.path.dirname(out_h5), exist_ok=True)

    trials = CMongoTrials(DB_NAME, exp_name + "_" + rbp, ip=HOST)

    # no trials yet - return None
    if trials.n_ok() == 0:
        trials = CMongoTrials(DB_NAME[:-2], exp_name + "_" + rbp, ip=HOST)
        if trials.n_ok() == 0:
            raise Exception("No trials")
    print("N trials: {0}".format(trials.n_ok()))

    # get best trial parameters
    tid = trials.best_trial_tid()
    model_path = trials.get_trial(tid)["result"]["path"]["model"]
    copyfile(model_path, out_h5) 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def save_model_all(model, save_dir, model_name, epoch):
    """
    :param model:  nn model
    :param save_dir: save model direction
    :param model_name:  model name
    :param epoch:  epoch
    :return:  None
    """
    if not os.path.isdir(save_dir):
        os.makedirs(save_dir)
    save_prefix = os.path.join(save_dir, model_name)
    save_path = '{}_epoch_{}.pt'.format(save_prefix, epoch)
    print("save all model to {}".format(save_path))
    output = open(save_path, mode="wb")
    torch.save(model.state_dict(), output)
    # torch.save(model.state_dict(), save_path)
    output.close() 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def save_best_model(model, save_dir, model_name, best_eval):
    """
    :param model:  nn model
    :param save_dir:  save model direction
    :param model_name:  model name
    :param best_eval:  eval best
    :return:  None
    """
    if best_eval.current_dev_score >= best_eval.best_dev_score:
        if not os.path.isdir(save_dir): os.makedirs(save_dir)
        model_name = "{}.pt".format(model_name)
        save_path = os.path.join(save_dir, model_name)
        print("save best model to {}".format(save_path))
        # if os.path.exists(save_path):  os.remove(save_path)
        output = open(save_path, mode="wb")
        torch.save(model.state_dict(), output)
        # torch.save(model.state_dict(), save_path)
        output.close()
        best_eval.early_current_patience = 0


# adjust lr 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def cache(self, dataset, cache, wait):
        """ Cache the given dataset if cache is enabled. Eventually waits for
        cache to be available (useful if another process is already computing
        cache) if provided wait flag is True.

        :param dataset: Dataset to be cached if cache is required.
        :param cache: Path of cache directory to be used, None if no cache.
        :param wait: If caching is enabled, True is cache should be waited.
        :returns: Cached dataset if needed, original dataset otherwise.
        """
        if cache is not None:
            if wait:
                while not exists(f'{cache}.index'):
                    get_logger().info(
                        'Cache not available, wait %s',
                        self.WAIT_PERIOD)
                    time.sleep(self.WAIT_PERIOD)
            cache_path = os.path.split(cache)[0]
            os.makedirs(cache_path, exist_ok=True)
            return dataset.cache(cache)
        return dataset 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def download_files(self, urls, env, headers=None, dir=None):

        if dir is None:
            dir = env.get('cache-dir')
        
        dest_directory = os.path.join(dir, "tmp_" + str(uuid.uuid4()))
        
        if not os.path.exists(dest_directory):
            os.makedirs(dest_directory)

        for data_url in urls:
            if isinstance(data_url, tuple):
                data_url, download_file = data_url
            else:
                download_file = data_url.split('/')[-1]

            download_path = os.path.join(dest_directory, download_file)

            if headers:
                opener = urllib.request.build_opener()
                opener.addheaders = headers
                urllib.request.install_opener(opener)

            try:
                urllib.request.urlretrieve(data_url, filename=download_path, 
                                           reporthook=self._report_hook(download_file), data=None)
            except Exception as e:
                raise VergeMLError("Could not download {}: {}".format(data_url, e))
            finally:
                if headers:
                    urllib.request.install_opener(urllib.request.build_opener())

        return dest_directory 

# 需要导入模块: import os [as 别名]
# 或者: from os import makedirs [as 别名]
def _get_cachedir():
    cachedir = os.path.join(util.get_tempdir(), 'cache')
    if not os.path.exists(cachedir):
        LOG.debug('Creating cache directory: ' + cachedir)
    if not os.path.exists(cachedir):
        os.makedirs(cachedir)
    return cachedir