deep-learning-with-pytorch/p2ch10/dsets.py

import copy
import csv
import functools
import glob
import os

from collections import namedtuple

import SimpleITK as sitk
import numpy as np

import torch
import torch.cuda
from torch.utils.data import Dataset

from util.disk import getCache
from util.util import XyzTuple, xyz2irc
from util.logconf import logging

log = logging.getLogger(__name__)
# log.setLevel(logging.WARN)
# log.setLevel(logging.INFO)
log.setLevel(logging.DEBUG)

raw_cache = getCache('part2ch09_raw')

NoduleInfoTuple = namedtuple('NoduleInfoTuple', 'isMalignant_bool, diameter_mm, series_uid, center_xyz')

@functools.lru_cache(1)
def getNoduleInfoList(requireDataOnDisk_bool=True):
    # We construct a set with all series_uids that are present on disk.
    # This will let us use the data, even if we haven't downloaded all of
    # the subsets yet.
    mhd_list = glob.glob('data-unversioned/part2/luna/subset*/*.mhd')
    dataPresentOnDisk_set = {os.path.split(p)[-1][:-4] for p in mhd_list}

    diameter_dict = {}
    with open('data/part2/luna/annotations.csv', "r") as f:
        for row in list(csv.reader(f))[1:]:
            series_uid = row[0]
            annotationCenter_xyz = tuple([float(x) for x in row[1:4]])
            annotationDiameter_mm = float(row[4])

            diameter_dict.setdefault(series_uid, []).append((annotationCenter_xyz, annotationDiameter_mm))

    noduleInfo_list = []
    with open('data/part2/luna/candidates.csv', "r") as f:
        for row in list(csv.reader(f))[1:]:
            series_uid = row[0]

            if series_uid not in dataPresentOnDisk_set and requireDataOnDisk_bool:
                continue

            isMalignant_bool = bool(int(row[4]))
            candidateCenter_xyz = tuple([float(x) for x in row[1:4]])

            candidateDiameter_mm = 0.0
            for annotationCenter_xyz, annotationDiameter_mm in diameter_dict.get(series_uid, []):
                for i in range(3):
                    delta_mm = abs(candidateCenter_xyz[i] - annotationCenter_xyz[i])
                    if delta_mm > annotationDiameter_mm / 4:
                        break
                else:
                    candidateDiameter_mm = annotationDiameter_mm
                    break

            noduleInfo_list.append(NoduleInfoTuple(isMalignant_bool, candidateDiameter_mm, series_uid, candidateCenter_xyz))

    noduleInfo_list.sort(reverse=True)
    return noduleInfo_list

class Ct(object):
    def __init__(self, series_uid):
        mhd_path = glob.glob('data-unversioned/part2/luna/subset*/{}.mhd'.format(series_uid))[0]

        ct_mhd = sitk.ReadImage(mhd_path)
        ct_a = np.array(sitk.GetArrayFromImage(ct_mhd), dtype=np.float32)

        # CTs are natively expressed in https://en.wikipedia.org/wiki/Hounsfield_scale
        # HU are scaled oddly, with 0 g/cc (air, approximately) being -1000 and 1 g/cc (water) being 0.
        # This gets rid of negative density stuff used to indicate out-of-FOV
        ct_a[ct_a < -1000] = -1000

        # This nukes any weird hotspots and clamps bone down
        ct_a[ct_a > 1000] = 1000

        self.series_uid = series_uid
        self.hu_a = ct_a

        self.origin_xyz = XyzTuple(*ct_mhd.GetOrigin())
        self.vxSize_xyz = XyzTuple(*ct_mhd.GetSpacing())
        self.direction_tup = tuple(int(round(x)) for x in ct_mhd.GetDirection())

    def getRawNodule(self, center_xyz, width_irc):
        center_irc = xyz2irc(center_xyz, self.origin_xyz, self.vxSize_xyz, self.direction_tup)

        slice_list = []
        for axis, center_val in enumerate(center_irc):
            start_ndx = int(round(center_val - width_irc[axis]/2))
            end_ndx = int(start_ndx + width_irc[axis])

            assert center_val >= 0 and center_val < self.hu_a.shape[axis], repr([self.series_uid, center_xyz, self.origin_xyz, self.vxSize_xyz, center_irc, axis])

            if start_ndx < 0:
                # log.warning("Crop outside of CT array: {} {}, center:{} shape:{} width:{}".format(
                #     self.series_uid, center_xyz, center_irc, self.hu_a.shape, width_irc))
                start_ndx = 0
                end_ndx = int(width_irc[axis])

            if end_ndx > self.hu_a.shape[axis]:
                # log.warning("Crop outside of CT array: {} {}, center:{} shape:{} width:{}".format(
                #     self.series_uid, center_xyz, center_irc, self.hu_a.shape, width_irc))
                end_ndx = self.hu_a.shape[axis]
                start_ndx = int(self.hu_a.shape[axis] - width_irc[axis])

            slice_list.append(slice(start_ndx, end_ndx))

        ct_chunk = self.hu_a[tuple(slice_list)]

        return ct_chunk, center_irc


@functools.lru_cache(1, typed=True)
def getCt(series_uid):
    return Ct(series_uid)

@raw_cache.memoize(typed=True)
def getCtRawNodule(series_uid, center_xyz, width_irc):
    ct = getCt(series_uid)
    ct_chunk, center_irc = ct.getRawNodule(center_xyz, width_irc)
    return ct_chunk, center_irc

class LunaDataset(Dataset):
    def __init__(self,
                 val_stride=0,
                 isValSet_bool=None,
                 series_uid=None,
            ):
        self.noduleInfo_list = copy.copy(getNoduleInfoList())

        if series_uid:
            self.noduleInfo_list = [x for x in self.noduleInfo_list if x.series_uid == series_uid]

        if isValSet_bool:
            assert val_stride > 0, val_stride
            self.noduleInfo_list = self.noduleInfo_list[::val_stride]
            assert self.noduleInfo_list
        elif val_stride > 0:
            del self.noduleInfo_list[::val_stride]
            assert self.noduleInfo_list

        log.info("{!r}: {} {} samples".format(
            self,
            len(self.noduleInfo_list),
            "validation" if isValSet_bool else "training",
        ))

    def __len__(self):
        return len(self.noduleInfo_list)

    def __getitem__(self, ndx):
        nodule_tup = self.noduleInfo_list[ndx]
        width_irc = (32, 48, 48)

        nodule_a, center_irc = getCtRawNodule(
            nodule_tup.series_uid,
            nodule_tup.center_xyz,
            width_irc,
        )

        nodule_t = torch.from_numpy(nodule_a)
        nodule_t = nodule_t.to(torch.float32)
        nodule_t = nodule_t.unsqueeze(0)

        malignant_t = torch.tensor([
                not nodule_tup.isMalignant_bool,
                nodule_tup.isMalignant_bool
            ],
            dtype=torch.long,
        )

        return nodule_t, malignant_t, nodule_tup.series_uid, torch.tensor(center_irc)