remove hub from mindspore master

mindspore/hub.py

@@ -1,214 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-hub for loading models:
-Users can load pre-trained models using mindspore.hub.load() API.
-"""
-import os
-import re
-import shutil
-import tarfile
-import hashlib
-from urllib.request import urlretrieve
-import requests
-from bs4 import BeautifulSoup
-
-import mindspore
-import mindspore.nn as nn
-from mindspore import log as logger
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
-
-DOWNLOAD_BASIC_URL = "http://download.mindspore.cn/model_zoo"
-OFFICIAL_NAME = "official"
-DEFAULT_CACHE_DIR = '.cache'
-MODEL_TARGET_CV = ['alexnet', 'fasterrcnn', 'googlenet', 'lenet', 'resnet', 'resnet50', 'ssd', 'vgg', 'yolo']
-MODEL_TARGET_NLP = ['bert', 'mass', 'transformer']
-
-
-def _packing_targz(output_filename, savepath=DEFAULT_CACHE_DIR):
-    """
-    Packing the input filename to filename.tar.gz in source dir.
-    """
-    try:
-        with tarfile.open(output_filename, "w:gz") as tar:
-            tar.add(savepath, arcname=os.path.basename(savepath))
-    except Exception as e:
-        raise OSError("Cannot tar file {} for - {}".format(output_filename, e))
-
-
-def _unpacking_targz(input_filename, savepath=DEFAULT_CACHE_DIR):
-    """
-    Unpacking the input filename to dirs.
-    """
-    try:
-        t = tarfile.open(input_filename)
-        t.extractall(path=savepath)
-    except Exception as e:
-        raise OSError("Cannot untar file {} for - {}".format(input_filename, e))
-
-
-def _remove_path_if_exists(path):
-    if os.path.exists(path):
-        if os.path.isfile(path):
-            os.remove(path)
-        else:
-            shutil.rmtree(path)
-
-
-def _create_path_if_not_exists(path):
-    if not os.path.exists(path):
-        if os.path.isfile(path):
-            os.remove(path)
-        else:
-            os.mkdir(path)
-
-
-def _get_weights_file(url, hash_md5=None, savepath=DEFAULT_CACHE_DIR):
-    """
-    get checkpoint weight from giving url.
-
-    Args:
-       url(string): checkpoint tar.gz url path.
-       hash_md5(string): checkpoint file md5.
-       savepath(string): checkpoint download save path.
-
-    Returns:
-       string.
-    """
-
-    def reporthook(a, b, c):
-        percent = a * b * 100.0 / c
-        show_str = ('[%%-%ds]' % 70) % (int(percent * 80) * '#')
-        print("\rDownloading:", show_str, " %5.1f%%" % (percent), end="")
-
-    def md5sum(file_name, hash_md5):
-        fp = open(file_name, 'rb')
-        content = fp.read()
-        fp.close()
-        m = hashlib.md5()
-        m.update(content.encode('utf-8'))
-        download_md5 = m.hexdigest()
-        return download_md5 == hash_md5
-
-    _remove_path_if_exists(os.path.realpath(savepath))
-    _create_path_if_not_exists(os.path.realpath(savepath))
-    ckpt_name = os.path.basename(url.split("/")[-1])
-    # identify file exist or not
-    file_path = os.path.join(savepath, ckpt_name)
-    if os.path.isfile(file_path):
-        if hash_md5 and md5sum(file_path, hash_md5):
-            print('File already exists!')
-            return file_path
-
-    file_path_ = file_path[:-7] if ".tar.gz" in file_path else file_path
-    _remove_path_if_exists(file_path_)
-
-    # download the checkpoint file
-    print('Downloading data from url {}'.format(url))
-    try:
-        urlretrieve(url, file_path, reporthook=reporthook)
-    except HTTPError as e:
-        raise Exception(e.code, e.msg, url)
-    except URLError as e:
-        raise Exception(e.errno, e.reason, url)
-    print('\nDownload finished!')
-
-    # untar file_path
-    _unpacking_targz(file_path, os.path.realpath(savepath))
-
-    filesize = os.path.getsize(file_path)
-    # turn the file size to Mb format
-    print('File size = %.2f Mb' % (filesize / 1024 / 1024))
-    return file_path_
-
-
-def _get_url_paths(url, ext='.tar.gz'):
-    response = requests.get(url)
-    if response.ok:
-        response_text = response.text
-    else:
-        return response.raise_for_status()
-    soup = BeautifulSoup(response_text, 'html.parser')
-    parent = [url + node.get('href') for node in soup.find_all('a')
-              if node.get('href').endswith(ext)]
-    return parent
-
-
-def _get_file_from_url(base_url, base_name):
-    idx = 0
-    urls = _get_url_paths(base_url + "/")
-    files = [url.split('/')[-1] for url in urls]
-    for i, name in enumerate(files):
-        if re.match(base_name + '*', name) is not None:
-            idx = i
-            break
-    return urls[idx]
-
-
-def load_weights(network, network_name=None, force_reload=True, **kwargs):
-    r"""
-    Load a model from mindspore, with pretrained weights.
-
-    Args:
-        network (Cell): Cell network.
-        network_name (string, optional): Cell network name get from network. Default: None.
-        force_reload (bool, optional): Whether to force a fresh download unconditionally. Default: False.
-        kwargs (dict, optional): The corresponding kwargs for download for model.
-
-            - device_target (str, optional): Runtime device target. Default: 'ascend'.
-            - dataset (str, optional): Dataset to train the network. Default: 'cifar10'.
-            - version (str, optional): MindSpore version to save the checkpoint. Default: Latest version.
-
-    Example:
-        >>> hub.load(network, network_name='lenet',
-                     **{'device_target': 'ascend', 'dataset':'mnist', 'version': '0.5.0'})
-    """
-    if not isinstance(network, nn.Cell):
-        logger.error("Failed to combine the net and the parameters.")
-        msg = ("Argument net should be a Cell, but got {}.".format(type(network)))
-        raise TypeError(msg)
-
-    if network_name is None:
-        if hasattr(network, network_name):
-            network_name = network.network_name
-        else:
-            msg = "Should input network name, but got None."
-            raise TypeError(msg)
-
-    device_target = kwargs['device_target'] if kwargs['device_target'] else 'ascend'
-    dataset = kwargs['dataset'] if kwargs['dataset'] else 'imagenet'
-    version = kwargs['version'] if kwargs['version'] else mindspore.version.__version__
-
-    if network_name.split("_")[0] in MODEL_TARGET_CV:
-        model_type = "cv"
-    elif network_name.split("_")[0] in MODEL_TARGET_NLP:
-        model_type = "nlp"
-    else:
-        raise ValueError("Unsupported network {} download checkpoint.".format(network_name.split("_")[0]))
-
-    download_base_url = "/".join([DOWNLOAD_BASIC_URL,
-                                  OFFICIAL_NAME, model_type, network_name])
-    download_file_name = "_".join(
-        [network_name, device_target, version, dataset, OFFICIAL_NAME])
-    download_url = _get_file_from_url(download_base_url, download_file_name)
-
-    if force_reload:
-        ckpt_path = _get_weights_file(download_url, None, DEFAULT_CACHE_DIR)
-    else:
-        raise ValueError("Unsupported not force reload.")
-
-    ckpt_file = os.path.join(ckpt_path, network_name + ".ckpt")
-    param_dict = load_checkpoint(ckpt_file)
-    load_param_into_net(network, param_dict)