Source code for mxnet.gluon.utils

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# coding: utf-8
# pylint: disable=
"""Parallelization utility optimizer."""
__all__ = ['split_data', 'split_and_load', 'clip_global_norm',
           'check_sha1', 'download']

import os
import hashlib
import warnings
    import requests
except ImportError:
    class requests_failed_to_import(object):
    requests = requests_failed_to_import

import numpy as np

from .. import ndarray

[docs]def split_data(data, num_slice, batch_axis=0, even_split=True): """Splits an NDArray into `num_slice` slices along `batch_axis`. Usually used for data parallelism where each slices is sent to one device (i.e. GPU). Parameters ---------- data : NDArray A batch of data. num_slice : int Number of desired slices. batch_axis : int, default 0 The axis along which to slice. even_split : bool, default True Whether to force all slices to have the same number of elements. If `True`, an error will be raised when `num_slice` does not evenly divide `data.shape[batch_axis]`. Returns ------- list of NDArray Return value is a list even if `num_slice` is 1. """ size = data.shape[batch_axis] if size < num_slice: raise ValueError( "Too many slices for data with shape %s. Arguments are " \ "num_slice=%d and batch_axis=%d."%(str(data.shape), num_slice, batch_axis)) if even_split and size % num_slice != 0: raise ValueError( "data with shape %s cannot be evenly split into %d slices along axis %d. " \ "Use a batch size that's multiple of %d or set even_split=False to allow " \ "uneven partitioning of data."%( str(data.shape), num_slice, batch_axis, num_slice)) step = size // num_slice if batch_axis == 0: slices = [data[i*step:(i+1)*step] if i < num_slice - 1 else data[i*step:size] for i in range(num_slice)] elif even_split: slices = ndarray.split(data, num_outputs=num_slice, axis=batch_axis) else: slices = [ndarray.slice_axis(data, batch_axis, i*step, (i+1)*step) if i < num_slice - 1 else ndarray.slice_axis(data, batch_axis, i*step, size) for i in range(num_slice)] return slices
[docs]def split_and_load(data, ctx_list, batch_axis=0, even_split=True): """Splits an NDArray into `len(ctx_list)` slices along `batch_axis` and loads each slice to one context in `ctx_list`. Parameters ---------- data : NDArray A batch of data. ctx_list : list of Context A list of Contexts. batch_axis : int, default 0 The axis along which to slice. even_split : bool, default True Whether to force all slices to have the same number of elements. Returns ------- list of NDArray Each corresponds to a context in `ctx_list`. """ if not isinstance(data, ndarray.NDArray): data = ndarray.array(data, ctx=ctx_list[0]) if len(ctx_list) == 1: return [data.as_in_context(ctx_list[0])] slices = split_data(data, len(ctx_list), batch_axis, even_split) return [i.as_in_context(ctx) for i, ctx in zip(slices, ctx_list)]
[docs]def clip_global_norm(arrays, max_norm): """Rescales NDArrays so that the sum of their 2-norm is smaller than `max_norm`. """ assert len(arrays) > 0 ctx = arrays[0].context total_norm = ndarray.add_n(*[, x).as_in_context(ctx) for x in (arr.reshape((-1,)) for arr in arrays)]) total_norm = ndarray.sqrt(total_norm).asscalar() if not np.isfinite(total_norm): warnings.warn(UserWarning('nan or inf is detected. Clipping results will be undefined.'), stacklevel=2) scale = max_norm / (total_norm + 1e-8) if scale < 1.0: for arr in arrays: arr *= scale return total_norm
def _indent(s_, numSpaces): """Indent string """ s = s_.split('\n') if len(s) == 1: return s_ first = s.pop(0) s = [first] + [(numSpaces * ' ') + line for line in s] s = '\n'.join(s) return s
[docs]def check_sha1(filename, sha1_hash): """Check whether the sha1 hash of the file content matches the expected hash. Parameters ---------- filename : str Path to the file. sha1_hash : str Expected sha1 hash in hexadecimal digits. Returns ------- bool Whether the file content matches the expected hash. """ sha1 = hashlib.sha1() with open(filename, 'rb') as f: while True: data = if not data: break sha1.update(data) return sha1.hexdigest() == sha1_hash
[docs]def download(url, path=None, overwrite=False, sha1_hash=None): """Download an given URL Parameters ---------- url : str URL to download path : str, optional Destination path to store downloaded file. By default stores to the current directory with same name as in url. overwrite : bool, optional Whether to overwrite destination file if already exists. sha1_hash : str, optional Expected sha1 hash in hexadecimal digits. Will ignore existing file when hash is specified but doesn't match. Returns ------- str The file path of the downloaded file. """ if path is None: fname = url.split('/')[-1] elif os.path.isdir(path): fname = os.path.join(path, url.split('/')[-1]) else: fname = path if overwrite or not os.path.exists(fname) or (sha1_hash and not check_sha1(fname, sha1_hash)): dirname = os.path.dirname(os.path.abspath(os.path.expanduser(fname))) if not os.path.exists(dirname): os.makedirs(dirname) print('Downloading %s from %s...'%(fname, url)) r = requests.get(url, stream=True) if r.status_code != 200: raise RuntimeError("Failed downloading url %s"%url) with open(fname, 'wb') as f: for chunk in r.iter_content(chunk_size=1024): if chunk: # filter out keep-alive new chunks f.write(chunk) if sha1_hash and not check_sha1(fname, sha1_hash): raise UserWarning('File {} is downloaded but the content hash does not match. ' \ 'The repo may be outdated or download may be incomplete. ' \ 'If the "repo_url" is overridden, consider switching to ' \ 'the default repo.'.format(fname)) return fname