# 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.
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import hashlib
import json
import math
import os
import pickle
import warnings
from collections import defaultdict
from itertools import product, starmap
from pathlib import PurePath
from typing import Dict, Generator, Iterable, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
from torch.utils.data import DistributedSampler as _TorchDistributedSampler
from torch.utils.data._utils.collate import default_collate
from monai.networks.layers.simplelayers import GaussianFilter
from monai.utils import (
MAX_SEED,
BlendMode,
NumpyPadMode,
ensure_tuple,
ensure_tuple_rep,
ensure_tuple_size,
first,
optional_import,
nib, _ = optional_import("nibabel")
[docs]def get_random_patch(
dims: Sequence[int], patch_size: Sequence[int], rand_state: Optional[np.random.RandomState] = None
) -> Tuple[slice, ...]:
Returns a tuple of slices to define a random patch in an array of shape `dims` with size `patch_size` or the as
close to it as possible within the given dimension. It is expected that `patch_size` is a valid patch for a source
of shape `dims` as returned by `get_valid_patch_size`.
Args:
dims: shape of source array
patch_size: shape of patch size to generate
rand_state: a random state object to generate random numbers from
Returns:
(tuple of slice): a tuple of slice objects defining the patch
# choose the minimal corner of the patch
rand_int = np.random.randint if rand_state is None else rand_state.randint
min_corner = tuple(rand_int(0, ms - ps + 1) if ms > ps else 0 for ms, ps in zip(dims, patch_size))
# create the slices for each dimension which define the patch in the source array
return tuple(slice(mc, mc + ps) for mc, ps in zip(min_corner, patch_size))
[docs]def iter_patch_slices(
dims: Sequence[int], patch_size: Union[Sequence[int], int], start_pos: Sequence[int] = ()
) -> Generator[Tuple[slice, ...], None, None]:
Yield successive tuples of slices defining patches of size `patch_size` from an array of dimensions `dims`. The
iteration starts from position `start_pos` in the array, or starting at the origin if this isn't provided. Each
patch is chosen in a contiguous grid using a first dimension as least significant ordering.
Args:
dims: dimensions of array to iterate over
patch_size: size of patches to generate slices for, 0 or None selects whole dimension
start_pos: starting position in the array, default is 0 for each dimension
Yields:
Tuples of slice objects defining each patch
# ensure patchSize and startPos are the right length
ndim = len(dims)
patch_size_ = get_valid_patch_size(dims, patch_size)
start_pos = ensure_tuple_size(start_pos, ndim)
# collect the ranges to step over each dimension
ranges = tuple(starmap(range, zip(start_pos, dims, patch_size_)))
# choose patches by applying product to the ranges
for position in product(*ranges[::-1]): # reverse ranges order to iterate in index order
yield tuple(slice(s, s + p) for s, p in zip(position[::-1], patch_size_))
[docs]def dense_patch_slices(
image_size: Sequence[int],
patch_size: Sequence[int],
scan_interval: Sequence[int],
) -> List[Tuple[slice, ...]]:
Enumerate all slices defining ND patches of size `patch_size` from an `image_size` input image.
Args:
image_size: dimensions of image to iterate over
patch_size: size of patches to generate slices
scan_interval: dense patch sampling interval
Returns:
a list of slice objects defining each patch
num_spatial_dims = len(image_size)
patch_size = get_valid_patch_size(image_size, patch_size)
scan_interval = ensure_tuple_size(scan_interval, num_spatial_dims)
scan_num = []
for i in range(num_spatial_dims):
if scan_interval[i] == 0:
scan_num.append(1)
else:
num = int(math.ceil(float(image_size[i]) / scan_interval[i]))
scan_dim = first(d for d in range(num) if d * scan_interval[i] + patch_size[i] >= image_size[i])
scan_num.append(scan_dim + 1 if scan_dim is not None else 1)
starts = []
for dim in range(num_spatial_dims):
dim_starts = []
for idx in range(scan_num[dim]):
start_idx = idx * scan_interval[dim]
start_idx -= max(start_idx + patch_size[dim] - image_size[dim], 0)
dim_starts.append(start_idx)
starts.append(dim_starts)
out = np.asarray([x.flatten() for x in np.meshgrid(*starts, indexing="ij")]).T
return [tuple(slice(s, s + patch_size[d]) for d, s in enumerate(x)) for x in out]
[docs]def iter_patch(
arr: np.ndarray,
patch_size: Union[Sequence[int], int] = 0,
start_pos: Sequence[int] = (),
copy_back: bool = True,
mode: Union[NumpyPadMode, str] = NumpyPadMode.WRAP,
**pad_opts: Dict,
) -> Generator[np.ndarray, None, None]:
Yield successive patches from `arr` of size `patch_size`. The iteration can start from position `start_pos` in `arr`
but drawing from a padded array extended by the `patch_size` in each dimension (so these coordinates can be negative
to start in the padded region). If `copy_back` is True the values from each patch are written back to `arr`.
Args:
arr: array to iterate over
patch_size: size of patches to generate slices for, 0 or None selects whole dimension
start_pos: starting position in the array, default is 0 for each dimension
copy_back: if True data from the yielded patches is copied back to `arr` once the generator completes
mode: {``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``, ``"mean"``,
``"median"``, ``"minimum"``, ``"reflect"``, ``"symmetric"``, ``"wrap"``, ``"empty"``}
One of the listed string values or a user supplied function. Defaults to ``"wrap"``.
See also: https://numpy.org/doc/1.18/reference/generated/numpy.pad.html
pad_opts: padding options, see `numpy.pad`
Yields:
Patches of array data from `arr` which are views into a padded array which can be modified, if `copy_back` is
True these changes will be reflected in `arr` once the iteration completes.
# ensure patchSize and startPos are the right length
patch_size_ = get_valid_patch_size(arr.shape, patch_size)
start_pos = ensure_tuple_size(start_pos, arr.ndim)
# pad image by maximum values needed to ensure patches are taken from inside an image
arrpad = np.pad(arr, tuple((p, p) for p in patch_size_), NumpyPadMode(mode).value, **pad_opts)
# choose a start position in the padded image
start_pos_padded = tuple(s + p for s, p in zip(start_pos, patch_size_))
# choose a size to iterate over which is smaller than the actual padded image to prevent producing
# patches which are only in the padded regions
iter_size = tuple(s + p for s, p in zip(arr.shape, patch_size_))
for slices in iter_patch_slices(iter_size, patch_size_, start_pos_padded):
yield arrpad[slices]
# copy back data from the padded image if required
if copy_back:
slices = tuple(slice(p, p + s) for p, s in zip(patch_size_, arr.shape))
arr[...] = arrpad[slices]
[docs]def get_valid_patch_size(image_size: Sequence[int], patch_size: Union[Sequence[int], int]) -> Tuple[int, ...]:
Given an image of dimensions `image_size`, return a patch size tuple taking the dimension from `patch_size` if this is
not 0/None. Otherwise, or if `patch_size` is shorter than `image_size`, the dimension from `image_size` is taken. This ensures
the returned patch size is within the bounds of `image_size`. If `patch_size` is a single number this is interpreted as a
patch of the same dimensionality of `image_size` with that size in each dimension.
ndim = len(image_size)
patch_size_ = ensure_tuple_size(patch_size, ndim)
# ensure patch size dimensions are not larger than image dimension, if a dimension is None or 0 use whole dimension
return tuple(min(ms, ps or ms) for ms, ps in zip(image_size, patch_size_))
[docs]def list_data_collate(batch: Sequence):
Enhancement for PyTorch DataLoader default collate.
If dataset already returns a list of batch data that generated in transforms, need to merge all data to 1 list.
Then it's same as the default collate behavior.
Note:
Need to use this collate if apply some transforms that can generate batch data.
elem = batch[0]
data = [i for k in batch for i in k] if isinstance(elem, list) else batch
return default_collate(data)
[docs]def worker_init_fn(worker_id: int) -> None:
Callback function for PyTorch DataLoader `worker_init_fn`.
It can set different random seed for the transforms in different workers.
worker_info = torch.utils.data.get_worker_info()
set_rnd(worker_info.dataset, seed=worker_info.seed)
[docs]def set_rnd(obj, seed: int) -> int:
Set seed or random state for all randomisable properties of obj.
Args:
seed: set the random state with an integer seed.
if not hasattr(obj, "__dict__"):
return seed # no attribute
if hasattr(obj, "set_random_state"):
obj.set_random_state(seed=seed % MAX_SEED)
return seed + 1 # a different seed for the next component
for key in obj.__dict__:
seed = set_rnd(obj.__dict__[key], seed=seed)
return seed
[docs]def zoom_affine(affine: np.ndarray, scale: Sequence[float], diagonal: bool = True) -> np.ndarray:
To make column norm of `affine` the same as `scale`. If diagonal is False,
returns an affine that combines orthogonal rotation and the new scale.
This is done by first decomposing `affine`, then setting the zoom factors to
`scale`, and composing a new affine; the shearing factors are removed. If
diagonal is True, returns a diagonal matrix, the scaling factors are set
to the diagonal elements. This function always return an affine with zero
translations.
Args:
affine (nxn matrix): a square matrix.
scale: new scaling factor along each dimension.
diagonal: whether to return a diagonal scaling matrix.
Defaults to True.
Raises:
ValueError: When ``affine`` is not a square matrix.
ValueError: When ``scale`` contains a nonpositive scalar.
Returns:
the updated `n x n` affine.
affine = np.array(affine, dtype=float, copy=True)
if len(affine) != len(affine[0]):
raise ValueError(f"affine must be n x n, got {len(affine)} x {len(affine[0])}.")
scale_np = np.array(scale, dtype=float, copy=True)
if np.any(scale_np <= 0):
raise ValueError("scale must contain only positive numbers.")
d = len(affine) - 1
if len(scale_np) < d: # defaults based on affine
norm = np.sqrt(np.sum(np.square(affine), 0))[:-1]
scale_np = np.append(scale_np, norm[len(scale_np) :])
scale_np = scale_np[:d]
scale_np[scale_np == 0] = 1.0
if diagonal:
return np.diag(np.append(scale_np, [1.0]))
rzs = affine[:-1, :-1] # rotation zoom scale
zs = np.linalg.cholesky(rzs.T @ rzs).T
rotation = rzs @ np.linalg.inv(zs)
s = np.sign(np.diag(zs)) * np.abs(scale_np)
# construct new affine with rotation and zoom
new_affine = np.eye(len(affine))
new_affine[:-1, :-1] = rotation @ np.diag(s)
return new_affine
[docs]def compute_shape_offset(
spatial_shape: np.ndarray, in_affine: np.ndarray, out_affine: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
Given input and output affine, compute appropriate shapes
in the output space based on the input array's shape.
This function also returns the offset to put the shape
in a good position with respect to the world coordinate system.
Args:
spatial_shape: input array's shape
in_affine (matrix): 2D affine matrix
out_affine (matrix): 2D affine matrix
shape = np.array(spatial_shape, copy=True, dtype=float)
sr = len(shape)
in_affine = to_affine_nd(sr, in_affine)
out_affine = to_affine_nd(sr, out_affine)
in_coords = [(0.0, dim - 1.0) for dim in shape]
corners = np.asarray(np.meshgrid(*in_coords, indexing="ij")).reshape((len(shape), -1))
corners = np.concatenate((corners, np.ones_like(corners[:1])))
corners = in_affine @ corners
corners_out = np.linalg.inv(out_affine) @ corners
corners_out = corners_out[:-1] / corners_out[-1]
out_shape = np.round(corners_out.ptp(axis=1) + 1.0)
if np.allclose(nib.io_orientation(in_affine), nib.io_orientation(out_affine)):
# same orientation, get translate from the origin
offset = in_affine @ ([0] * sr + [1])
offset = offset[:-1] / offset[-1]
else:
# different orientation, the min is the origin
corners = corners[:-1] / corners[-1]
offset = np.min(corners, 1)
return out_shape.astype(int), offset
[docs]def to_affine_nd(r: Union[np.ndarray, int], affine: np.ndarray) -> np.ndarray:
Using elements from affine, to create a new affine matrix by
assigning the rotation/zoom/scaling matrix and the translation vector.
when ``r`` is an integer, output is an (r+1)x(r+1) matrix,
where the top left kxk elements are copied from ``affine``,
the last column of the output affine is copied from ``affine``'s last column.
`k` is determined by `min(r, len(affine) - 1)`.
when ``r`` is an affine matrix, the output has the same as ``r``,
the top left kxk elements are copied from ``affine``,
the last column of the output affine is copied from ``affine``'s last column.
`k` is determined by `min(len(r) - 1, len(affine) - 1)`.
Args:
r (int or matrix): number of spatial dimensions or an output affine to be filled.
affine (matrix): 2D affine matrix
Raises:
ValueError: When ``affine`` dimensions is not 2.
ValueError: When ``r`` is nonpositive.
Returns:
an (r+1) x (r+1) matrix
affine_np = np.array(affine, dtype=np.float64)
if affine_np.ndim != 2:
raise ValueError(f"affine must have 2 dimensions, got {affine_np.ndim}.")
new_affine = np.array(r, dtype=np.float64, copy=True)
if new_affine.ndim == 0:
sr = new_affine.astype(int)
if not np.isfinite(sr) or sr < 0:
raise ValueError(f"r must be positive, got {sr}.")
new_affine = np.eye(sr + 1, dtype=np.float64)
d = max(min(len(new_affine) - 1, len(affine_np) - 1), 1)
new_affine[:d, :d] = affine_np[:d, :d]
if d > 1:
new_affine[:d, -1] = affine_np[:d, -1]
return new_affine
[docs]def create_file_basename(
postfix: str,
input_file_name: str,
folder_path: str,
data_root_dir: str = "",
) -> str:
Utility function to create the path to the output file based on the input
filename (file name extension is not added by this function).
When `data_root_dir` is not specified, the output file name is:
`folder_path/input_file_name (no ext.) /input_file_name (no ext.)[_postfix]`
otherwise the relative path with respect to `data_root_dir` will be inserted.
Args:
postfix: output name's postfix
input_file_name: path to the input image file.
folder_path: path for the output file
data_root_dir: if not empty, it specifies the beginning parts of the input file's
absolute path. This is used to compute `input_file_rel_path`, the relative path to the file from
`data_root_dir` to preserve folder structure when saving in case there are files in different
folders with the same file names.
# get the filename and directory
filedir, filename = os.path.split(input_file_name)
# remove extension
filename, ext = os.path.splitext(filename)
if ext == ".gz":
filename, ext = os.path.splitext(filename)
# use data_root_dir to find relative path to file
filedir_rel_path = ""
if data_root_dir and filedir:
filedir_rel_path = os.path.relpath(filedir, data_root_dir)
# sub-folder path will be original name without the extension
subfolder_path = os.path.join(folder_path, filedir_rel_path, filename)
if not os.path.exists(subfolder_path):
os.makedirs(subfolder_path)
if postfix:
# add the sub-folder plus the postfix name to become the file basename in the output path
output = os.path.join(subfolder_path, filename + "_" + postfix)
else:
output = os.path.join(subfolder_path, filename)
return os.path.abspath(output)
[docs]def compute_importance_map(
patch_size: Tuple[int, ...],
mode: Union[BlendMode, str] = BlendMode.CONSTANT,
sigma_scale: Union[Sequence[float], float] = 0.125,
device: Union[torch.device, int, str] = "cpu",
) -> torch.Tensor:
"""Get importance map for different weight modes.
Args:
patch_size: Size of the required importance map. This should be either H, W [,D].
mode: {``"constant"``, ``"gaussian"``}
How to blend output of overlapping windows. Defaults to ``"constant"``.
- ``"constant``": gives equal weight to all predictions.
- ``"gaussian``": gives less weight to predictions on edges of windows.
sigma_scale: Sigma_scale to calculate sigma for each dimension
(sigma = sigma_scale * dim_size). Used for gaussian mode only.
device: Device to put importance map on.
Raises:
ValueError: When ``mode`` is not one of ["constant", "gaussian"].
Returns:
Tensor of size patch_size.
mode = BlendMode(mode)
device = torch.device(device) # type: ignore[arg-type]
if mode == BlendMode.CONSTANT:
importance_map = torch.ones(patch_size, device=device).float()
elif mode == BlendMode.GAUSSIAN:
center_coords = [i // 2 for i in patch_size]
sigma_scale = ensure_tuple_rep(sigma_scale, len(patch_size))
sigmas = [i * sigma_s for i, sigma_s in zip(patch_size, sigma_scale)]
importance_map = torch.zeros(patch_size, device=device)
importance_map[tuple(center_coords)] = 1
pt_gaussian = GaussianFilter(len(patch_size), sigmas).to(device=device, dtype=torch.float)
importance_map = pt_gaussian(importance_map.unsqueeze(0).unsqueeze(0))
importance_map = importance_map.squeeze(0).squeeze(0)
importance_map = importance_map / torch.max(importance_map)
importance_map = importance_map.float()
# importance_map cannot be 0, otherwise we may end up with nans!
min_non_zero = importance_map[importance_map != 0].min().item()
importance_map = torch.clamp(importance_map, min=min_non_zero)
else:
raise ValueError(
f"Unsupported mode: {mode}, available options are [{BlendMode.CONSTANT}, {BlendMode.CONSTANT}]."
return importance_map
[docs]def partition_dataset(
data: Sequence,
ratios: Optional[Sequence[float]] = None,
num_partitions: Optional[int] = None,
shuffle: bool = False,
seed: int = 0,
drop_last: bool = False,
even_divisible: bool = False,
Split the dataset into N partitions. It can support shuffle based on specified random seed.
Will return a set of datasets, every dataset contains 1 partition of original dataset.
And it can split the dataset based on specified ratios or evenly split into `num_partitions`.
Refer to: https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py.
Args:
data: input dataset to split, expect a list of data.
ratios: a list of ratio number to split the dataset, like [8, 1, 1].
num_partitions: expected number of the partitions to evenly split, only works when `ratios` not specified.
shuffle: whether to shuffle the original dataset before splitting.
seed: random seed to shuffle the dataset, only works when `shuffle` is True.
drop_last: only works when `even_divisible` is False and no ratios specified.
if True, will drop the tail of the data to make it evenly divisible across partitions.
if False, will add extra indices to make the data evenly divisible across partitions.
even_divisible: if True, guarantee every partition has same length.
Examples::
>>> data = [1, 2, 3, 4, 5]
>>> partition_dataset(data, ratios=[0.6, 0.2, 0.2], shuffle=False)
[[1, 2, 3], [4], [5]]
>>> partition_dataset(data, num_partitions=2, shuffle=False)
[[1, 3, 5], [2, 4]]
>>> partition_dataset(data, num_partitions=2, shuffle=False, even_divisible=True, drop_last=True)
[[1, 3], [2, 4]]
>>> partition_dataset(data, num_partitions=2, shuffle=False, even_divisible=True, drop_last=False)
[[1, 3, 5], [2, 4, 1]]
>>> partition_dataset(data, num_partitions=2, shuffle=False, even_divisible=False, drop_last=False)
[[1, 3, 5], [2, 4]]
data_len = len(data)
datasets = []
indices = list(range(data_len))
if shuffle:
# deterministically shuffle based on fixed seed for every process
rs = np.random.RandomState(seed)
rs.shuffle(indices)
if ratios:
next_idx = 0
rsum = sum(ratios)
for r in ratios:
start_idx = next_idx
next_idx = min(start_idx + int(r / rsum * data_len + 0.5), data_len)
datasets.append([data[i] for i in indices[start_idx:next_idx]])
return datasets
if not num_partitions:
raise ValueError("must specify number of partitions or ratios.")
# evenly split the data without ratios
if not even_divisible and drop_last:
raise RuntimeError("drop_last only works when even_divisible is True.")
if data_len < num_partitions:
raise RuntimeError(f"there is no enough data to be split into {num_partitions} partitions.")
if drop_last and data_len % num_partitions != 0:
# split to nearest available length that is evenly divisible
num_samples = math.ceil((data_len - num_partitions) / num_partitions)
else:
num_samples = math.ceil(data_len / num_partitions)
# use original data length if not even divisible
total_size = num_samples * num_partitions if even_divisible else data_len
if not drop_last and total_size - data_len > 0:
# add extra samples to make it evenly divisible
indices += indices[: (total_size - data_len)]
else:
# remove tail of data to make it evenly divisible
indices = indices[:total_size]
for i in range(num_partitions):
_indices = indices[i:total_size:num_partitions]
datasets.append([data[j] for j in _indices])
return datasets
[docs]def partition_dataset_classes(
data: Sequence,
classes: Sequence[int],
ratios: Optional[Sequence[float]] = None,
num_partitions: Optional[int] = None,
shuffle: bool = False,
seed: int = 0,
drop_last: bool = False,
even_divisible: bool = False,
Split the dataset into N partitions based on the given class labels.
It can make sure the same ratio of classes in every partition.
Others are same as :py:class:`monai.data.partition_dataset`.
Args:
data: input dataset to split, expect a list of data.
classes: a list of labels to help split the data, the length must match the length of data.
ratios: a list of ratio number to split the dataset, like [8, 1, 1].
num_partitions: expected number of the partitions to evenly split, only works when no `ratios`.
shuffle: whether to shuffle the original dataset before splitting.
seed: random seed to shuffle the dataset, only works when `shuffle` is True.
drop_last: only works when `even_divisible` is False and no ratios specified.
if True, will drop the tail of the data to make it evenly divisible across partitions.
if False, will add extra indices to make the data evenly divisible across partitions.
even_divisible: if True, guarantee every partition has same length.
Examples::
>>> data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]
>>> classes = [2, 0, 2, 1, 3, 2, 2, 0, 2, 0, 3, 3, 1, 3]
>>> partition_dataset_classes(data, classes, shuffle=False, ratios=[2, 1])
[[2, 8, 4, 1, 3, 6, 5, 11, 12], [10, 13, 7, 9, 14]]
if not classes or len(classes) != len(data):
raise ValueError(f"length of classes {classes} must match the dataset length {len(data)}.")
datasets = []
class_indices = defaultdict(list)
for i, c in enumerate(classes):
class_indices[c].append(i)
class_partition_indices: List[Sequence] = list()
for _, per_class_indices in sorted(class_indices.items()):
per_class_partition_indices = partition_dataset(
data=per_class_indices,
ratios=ratios,
num_partitions=num_partitions,
shuffle=shuffle,
seed=seed,
drop_last=drop_last,
even_divisible=even_divisible,
if not class_partition_indices:
class_partition_indices = per_class_partition_indices
else:
for part, data_indices in zip(class_partition_indices, per_class_partition_indices):
part += data_indices
rs = np.random.RandomState(seed)
for indices in class_partition_indices:
if shuffle:
rs.shuffle(indices)
datasets.append([data[j] for j in indices])
return datasets
[docs]def select_cross_validation_folds(partitions: Sequence[Iterable], folds: Union[Sequence[int], int]) -> List:
Select cross validation data based on data partitions and specified fold index.
if a list of fold indices is provided, concatenate the partitions of these folds.
Args:
partitions: a sequence of datasets, each item is a iterable
folds: the indices of the partitions to be combined.
Returns:
A list of combined datasets.
Example::
>>> partitions = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]]
>>> select_cross_validation_folds(partitions, 2)
[5, 6]
>>> select_cross_validation_folds(partitions, [1, 2])
[3, 4, 5, 6]
>>> select_cross_validation_folds(partitions, [-1, 2])
[9, 10, 5, 6]
return [data_item for fold_id in ensure_tuple(folds) for data_item in partitions[fold_id]]
[docs]class DistributedSampler(_TorchDistributedSampler):
Enhance PyTorch DistributedSampler to support non-evenly divisible sampling.
Args:
even_divisible: if False, different ranks can have different data length.
for example, input data: [1, 2, 3, 4, 5], rank 0: [1, 3, 5], rank 1: [2, 4].
More information about DistributedSampler, please check:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
def __init__(self, even_divisible: bool = True, *args, **kwargs):
self.total_size: int = 0
self.rank: int = 0
self.num_samples: int = 0
self.num_replicas: int = 0
super().__init__(*args, **kwargs)
if not even_divisible:
data_len = len(kwargs["dataset"])
extra_size = self.total_size - data_len
if self.rank + extra_size >= self.num_replicas:
self.num_samples -= 1
self.total_size = data_len
[docs]def json_hashing(item) -> bytes:
Args:
item: data item to be hashed
Returns: the corresponding hash key
# TODO: Find way to hash transforms content as part of the cache
cache_key = hashlib.md5(json.dumps(item, sort_keys=True).encode("utf-8")).hexdigest()
return f"{cache_key}".encode("utf-8")
[docs]def pickle_hashing(item, protocol=pickle.HIGHEST_PROTOCOL) -> bytes:
Args:
item: data item to be hashed
protocol: protocol version used for pickling,
defaults to `pickle.HIGHEST_PROTOCOL`.
Returns: the corresponding hash key
cache_key = hashlib.md5(pickle.dumps(sorted_dict(item), protocol=protocol)).hexdigest()
return f"{cache_key}".encode("utf-8")
[docs]def sorted_dict(item, key=None, reverse=False):
"""Return a new sorted dictionary from the `item`."""
if not isinstance(item, dict):
return item
return {k: sorted_dict(v) if isinstance(v, dict) else v for k, v in sorted(item.items(), key=key, reverse=reverse)}
