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import collections
import importlib
import h5py
import numpy as np
import torch
from import Dataset, DataLoader, ConcatDataset
import augment.transforms as transforms
from unet3d.utils import get_logger
class SliceBuilder:
def __init__(self, raw_datasets, label_datasets, weight_dataset, patch_shape, stride_shape):
self._raw_slices = self._build_slices(raw_datasets[0], patch_shape, stride_shape)
if label_datasets is None:
self._label_slices = None
# take the first element in the label_datasets to build slices
self._label_slices = self._build_slices(label_datasets[0], patch_shape, stride_shape)
assert len(self._raw_slices) == len(self._label_slices)
if weight_dataset is None:
self._weight_slices = None
self._weight_slices = self._build_slices(weight_dataset[0], patch_shape, stride_shape)
assert len(self.raw_slices) == len(self._weight_slices)
def raw_slices(self):
return self._raw_slices
def label_slices(self):
return self._label_slices
def weight_slices(self):
return self._weight_slices
def _build_slices(dataset, patch_shape, stride_shape):
"""Iterates over a given n-dim dataset patch-by-patch with a given stride
and builds an array of slice positions.
list of slices, i.e.
[(slice, slice, slice, slice), ...] if len(shape) == 4
[(slice, slice, slice), ...] if len(shape) == 3
slices = []
if dataset.ndim == 4:
in_channels, i_z, i_y, i_x = dataset.shape
i_z, i_y, i_x = dataset.shape
k_z, k_y, k_x = patch_shape
s_z, s_y, s_x = stride_shape
z_steps = SliceBuilder._gen_indices(i_z, k_z, s_z)
for z in z_steps:
y_steps = SliceBuilder._gen_indices(i_y, k_y, s_y)
for y in y_steps:
x_steps = SliceBuilder._gen_indices(i_x, k_x, s_x)
for x in x_steps:
slice_idx = (
slice(z, z + k_z),
slice(y, y + k_y),
slice(x, x + k_x)
if dataset.ndim == 4:
slice_idx = (slice(0, in_channels),) + slice_idx
return slices
def _gen_indices(i, k, s):
assert i >= k, 'Sample size has to be bigger than the patch size'
for j in range(0, i - k + 1, s):
yield j
if j + k < i:
yield i - k
class FilterSliceBuilder(SliceBuilder):
Filter patches containing more than `1 - threshold` of ignore_index label
def __init__(self, raw_datasets, label_datasets, weight_datasets, patch_shape, stride_shape, ignore_index=(0,),
threshold=0.8, slack_acceptance=0.01):
super().__init__(raw_datasets, label_datasets, weight_datasets, patch_shape, stride_shape)
if label_datasets is None:
def ignore_predicate(raw_label_idx):
label_idx = raw_label_idx[1]
patch = label_datasets[0][label_idx]
non_ignore_counts = np.array([np.count_nonzero(patch != ii) for ii in ignore_index])
non_ignore_counts = non_ignore_counts / patch.size
return np.any(non_ignore_counts > threshold) or np.random.rand() < slack_acceptance
zipped_slices = zip(self.raw_slices, self.label_slices)
# ignore slices containing too much ignore_index
filtered_slices = list(filter(ignore_predicate, zipped_slices))
# unzip and save slices
raw_slices, label_slices = zip(*filtered_slices)
self._raw_slices = list(raw_slices)
self._label_slices = list(label_slices)
class HDF5Dataset(Dataset):
Implementation of backed by the HDF5 files, which iterates over the raw and label datasets
patch by patch with a given stride.
def __init__(self, file_path, patch_shape, stride_shape, phase, transformer_config,
raw_internal_path='raw', label_internal_path='label',
weight_internal_path=None, slice_builder_cls=SliceBuilder):
:param file_path: path to H5 file containing raw data as well as labels and per pixel weights (optional)
:param patch_shape: the shape of the patch DxHxW
:param stride_shape: the shape of the stride DxHxW
:param phase: 'train' for training, 'val' for validation, 'test' for testing; data augmentation is performed
only during the 'train' phase
:param transformer_config: data augmentation configuration
:param raw_internal_path (str or list): H5 internal path to the raw dataset
:param label_internal_path (str or list): H5 internal path to the label dataset
:param weight_internal_path (str or list): H5 internal path to the per pixel weights
:param slice_builder_cls: defines how to sample the patches from the volume
assert phase in ['train', 'val', 'test']
self.phase = phase
self.file_path = file_path
# convert raw_internal_path, label_internal_path and weight_internal_path to list for ease of computation
if isinstance(raw_internal_path, str):
raw_internal_path = [raw_internal_path]
if isinstance(label_internal_path, str):
label_internal_path = [label_internal_path]
if isinstance(weight_internal_path, str):
weight_internal_path = [weight_internal_path]
with h5py.File(file_path, 'r') as input_file:
# WARN: we load everything into memory due to hdf5 bug when reading H5 from multiple subprocesses, i.e.
# File "h5py/_proxy.pyx", line 84, in h5py._proxy.H5PY_H5Dread
# OSError: Can't read data (inflate() failed)
self.raws = [input_file[internal_path][...] for internal_path in raw_internal_path]
# calculate global mean and std for Normalization augmentation
mean, std = self._calculate_mean_std(self.raws[0])
self.transformer = transforms.get_transformer(transformer_config, mean, std, phase)
self.raw_transform = self.transformer.raw_transform()
if phase != 'test':
# create label/weight transform only in train/val phase
self.label_transform = self.transformer.label_transform()
self.labels = [input_file[internal_path][...] for internal_path in label_internal_path]
if weight_internal_path is not None:
# look for the weight map in the raw file
self.weight_maps = [input_file[internal_path][...] for internal_path in weight_internal_path]
self.weight_transform = self.transformer.weight_transform()
self.weight_maps = None
self._check_dimensionality(self.raws, self.labels)
# 'test' phase used only for predictions so ignore the label dataset
self.labels = None
self.weight_maps = None
# build slice indices for raw and label data sets
slice_builder = slice_builder_cls(self.raws, self.labels, self.weight_maps, patch_shape, stride_shape)
self.raw_slices = slice_builder.raw_slices
self.label_slices = slice_builder.label_slices
self.weight_slices = slice_builder.weight_slices
self.patch_count = len(self.raw_slices)
def __getitem__(self, idx):
if idx >= len(self):
raise StopIteration
# get the slice for a given index 'idx'
raw_idx = self.raw_slices[idx]
# get the raw data patch for a given slice
raw_patch_transformed = self._transform_patches(self.raws, raw_idx, self.raw_transform)
if self.phase == 'test':
# just return the transformed raw patch and the metadata
return raw_patch_transformed, raw_idx
# get the slice for a given index 'idx'
label_idx = self.label_slices[idx]
label_patch_transformed = self._transform_patches(self.labels, label_idx, self.label_transform)
if self.weight_maps is not None:
weight_idx = self.weight_slices[idx]
# return the transformed weight map for a given patch together with raw and label data
weight_patch_transformed = self._transform_patches(self.weight_maps, weight_idx, self.weight_transform)
return raw_patch_transformed, label_patch_transformed, weight_patch_transformed
# return the transformed raw and label patches
return raw_patch_transformed, label_patch_transformed
def _transform_patches(datasets, label_idx, transformer):
transformed_patches = []
for dataset in datasets:
# get the label data and apply the label transformer
transformed_patch = transformer(dataset[label_idx])
# if transformed_patches is a singleton list return the first element only
if len(transformed_patches) == 1:
return transformed_patches[0]
return transformed_patches
def __len__(self):
return self.patch_count
def _calculate_mean_std(input):
Compute a mean/std of the raw stack for normalization.
This is an in-memory implementation, override this method
with the chunk-based computation if you're working with huge H5 files.
:return: a tuple of (mean, std) of the raw data
return input.mean(keepdims=True), input.std(keepdims=True)
def _check_dimensionality(raws, labels):
for raw in raws:
assert raw.ndim in [3, 4], 'Raw dataset must be 3D (DxHxW) or 4D (CxDxHxW)'
if raw.ndim == 3:
raw_shape = raw.shape
raw_shape = raw.shape[1:]
for label in labels:
assert label.ndim in [3, 4], 'Label dataset must be 3D (DxHxW) or 4D (CxDxHxW)'
if label.ndim == 3:
label_shape = label.shape
label_shape = label.shape[1:]
assert raw_shape == label_shape, 'Raw and labels have to be of the same size'
def _check_patch_shape(patch_shape):
assert len(patch_shape) == 3, 'patch_shape must be a 3D tuple'
assert patch_shape[1] >= 64 and patch_shape[2] >= 64, 'Height and Width must be greater or equal 64'
assert patch_shape[0] >= 16, 'Depth must be greater or equal 16'
def _get_slice_builder_cls(class_name):
m = importlib.import_module('datasets.hdf5')
clazz = getattr(m, class_name)
return clazz
def get_train_loaders(config):
Returns dictionary containing the training and validation loaders
( backed by the datasets.hdf5.HDF5Dataset.
:param config: a top level configuration object containing the 'loaders' key
:return: dict {
'train': <train_loader>
'val': <val_loader>
assert 'loaders' in config, 'Could not find data loaders configuration'
loaders_config = config['loaders']
logger = get_logger('HDF5Dataset')'Creating training and validation set loaders...')
# get train and validation files
train_paths = loaders_config['train_path']
val_paths = loaders_config['val_path']
assert isinstance(train_paths, list)
assert isinstance(val_paths, list)
# get h5 internal paths for raw and label
raw_internal_path = loaders_config['raw_internal_path']
label_internal_path = loaders_config['label_internal_path']
weight_internal_path = loaders_config.get('weight_internal_path', None)
# get train/validation patch size and stride
train_patch = tuple(loaders_config['train_patch'])
train_stride = tuple(loaders_config['train_stride'])
val_patch = tuple(loaders_config['val_patch'])
val_stride = tuple(loaders_config['val_stride'])
# get slice_builder_cls
slice_builder_str = loaders_config.get('slice_builder', 'SliceBuilder')'Slice builder class: {slice_builder_str}')
slice_builder_cls = _get_slice_builder_cls(slice_builder_str)
train_datasets = []
for train_path in train_paths:
try:'Loading training set from: {train_path}...')
# create H5 backed training and validation dataset with data augmentation
train_dataset = HDF5Dataset(train_path, train_patch, train_stride, phase='train',
except Exception:'Skipping training set: {train_path}', exc_info=True)
val_datasets = []
for val_path in val_paths:
try:'Loading validation set from: {val_path}...')
val_dataset = HDF5Dataset(val_path, val_patch, val_stride, phase='val',
except Exception:'Skipping validation set: {val_path}', exc_info=True)
num_workers = loaders_config.get('num_workers', 1)'Number of workers for train/val datasets: {num_workers}')
# when training with volumetric data use batch_size of 1 due to GPU memory constraints
return {
'train': DataLoader(ConcatDataset(train_datasets), batch_size=1, shuffle=True, num_workers=num_workers),
'val': DataLoader(ConcatDataset(val_datasets), batch_size=1, shuffle=True, num_workers=num_workers)
def get_test_loaders(config):
Returns a list of DataLoader, one per each test file.
:param config: a top level configuration object containing the 'datasets' key
:return: generator of DataLoader objects
def my_collate(batch):
error_msg = "batch must contain tensors or slice; found {}"
if isinstance(batch[0], torch.Tensor):
return torch.stack(batch, 0)
elif isinstance(batch[0], slice):
return batch[0]
elif isinstance(batch[0], collections.Sequence):
transposed = zip(*batch)
return [my_collate(samples) for samples in transposed]
raise TypeError((error_msg.format(type(batch[0]))))
logger = get_logger('HDF5Dataset')
assert 'datasets' in config, 'Could not find data sets configuration'
datasets_config = config['datasets']
# get train and validation files
test_paths = datasets_config['test_path']
assert isinstance(test_paths, list)
# get h5 internal path
raw_internal_path = datasets_config['raw_internal_path']
# get train/validation patch size and stride
patch = tuple(datasets_config['patch'])
stride = tuple(datasets_config['stride'])
num_workers = datasets_config.get('num_workers', 1)
# construct datasets lazily
datasets = (HDF5Dataset(test_path, patch, stride, phase='test', raw_internal_path=raw_internal_path,
transformer_config=datasets_config['transformer']) for test_path in test_paths)
# use generator in order to create data loaders lazily one by one
for dataset in datasets:'Loading test set from: {dataset.file_path}...')
yield DataLoader(dataset, batch_size=1, num_workers=num_workers, collate_fn=my_collate)
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