deep-learning-with-pytorch/p2ch14/dsets.py

import copy
import csv
import functools
import glob
import math
import os
import random

from collections import namedtuple

import SimpleITK as sitk
import numpy as np

import torch
import torch.cuda
import torch.nn.functional as F
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('part2ch14_raw')

CandidateInfoTuple = namedtuple('CandidateInfoTuple', 'isNodule_bool, hasAnnotation_bool, isMal_bool, diameter_mm, series_uid, center_xyz')
MaskTuple = namedtuple('MaskTuple', 'raw_dense_mask, dense_mask, body_mask, air_mask, raw_candidate_mask, candidate_mask, lung_mask, neg_mask, pos_mask')

@functools.lru_cache(1)
def getCandidateInfoList(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}

    candidateInfo_list = []
    with open('data/part2/luna/annotations_with_malignancy.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])
            isMal_bool = {'False': False, 'True': True}[row[5]]

            candidateInfo_list.append(CandidateInfoTuple(True, True, isMal_bool, annotationDiameter_mm, series_uid, annotationCenter_xyz))

    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

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

            if not isNodule_bool:
                candidateInfo_list.append(CandidateInfoTuple(False, False, False, 0.0, series_uid, candidateCenter_xyz))

    candidateInfo_list.sort(reverse=True)
    return candidateInfo_list

@functools.lru_cache(1)
def getCandidateInfoDict(requireDataOnDisk_bool=True):
    candidateInfo_list = getCandidateInfoList(requireDataOnDisk_bool)
    candidateInfo_dict = {}

    for candidateInfo_tup in candidateInfo_list:
        candidateInfo_dict.setdefault(candidateInfo_tup.series_uid, []).append(candidateInfo_tup)

    return candidateInfo_dict


class Ct:
    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.
        # The lower bound gets rid of negative density stuff used to indicate out-of-FOV
        # The upper bound nukes any weird hotspots and clamps bone down
        ct_a.clip(-1000, 1000, ct_a)

        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_a = np.array(ct_mhd.GetDirection()).reshape(3, 3)

    def getRawCandidate(self, center_xyz, width_irc):
        center_irc = xyz2irc(center_xyz, self.origin_xyz, self.vxSize_xyz, self.direction_a)

        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 getCtRawCandidate(series_uid, center_xyz, width_irc):
    ct = getCt(series_uid)
    ct_chunk, center_irc = ct.getRawCandidate(center_xyz, width_irc)
    return ct_chunk, center_irc

@raw_cache.memoize(typed=True)
def getCtSampleSize(series_uid):
    ct = Ct(series_uid, buildMasks_bool=False)
    return len(ct.negative_indexes)

def getCtAugmentedCandidate(
        augmentation_dict,
        series_uid, center_xyz, width_irc,
        use_cache=True):
    if use_cache:
        ct_chunk, center_irc = getCtRawCandidate(series_uid, center_xyz, width_irc)
    else:
        ct = getCt(series_uid)
        ct_chunk, center_irc = ct.getRawCandidate(center_xyz, width_irc)

    ct_t = torch.tensor(ct_chunk).unsqueeze(0).unsqueeze(0).to(torch.float32)

    transform_t = torch.eye(4)
    # ... <1>

    for i in range(3):
        if 'flip' in augmentation_dict:
            if random.random() > 0.5:
                transform_t[i,i] *= -1

        if 'offset' in augmentation_dict:
            offset_float = augmentation_dict['offset']
            random_float = (random.random() * 2 - 1)
            transform_t[i, 3] = offset_float * random_float

        if 'scale' in augmentation_dict:
            scale_float = augmentation_dict['scale']
            random_float = (random.random() * 2 - 1)
            transform_t[i,i] *= 1.0 + scale_float * random_float


    if 'rotate' in augmentation_dict:
        angle_rad = random.random() * math.pi * 2
        s = math.sin(angle_rad)
        c = math.cos(angle_rad)

        rotation_t = torch.tensor([
            [c, -s, 0, 0],
            [s, c, 0, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
        ])

        transform_t @= rotation_t

    affine_t = F.affine_grid(
            transform_t[:3].unsqueeze(0).to(torch.float32),
            ct_t.size(),
            align_corners=False,
        )

    augmented_chunk = F.grid_sample(
            ct_t,
            affine_t,
            padding_mode='border',
            align_corners=False,
        ).to('cpu')

    if 'noise' in augmentation_dict:
        noise_t = torch.randn_like(augmented_chunk)
        noise_t *= augmentation_dict['noise']

        augmented_chunk += noise_t

    return augmented_chunk[0], center_irc


class LunaDataset(Dataset):
    def __init__(self,
                 val_stride=0,
                 isValSet_bool=None,
                 series_uid=None,
                 sortby_str='random',
                 ratio_int=0,
                 augmentation_dict=None,
                 candidateInfo_list=None,
            ):
        self.ratio_int = ratio_int
        self.augmentation_dict = augmentation_dict

        if candidateInfo_list:
            self.candidateInfo_list = copy.copy(candidateInfo_list)
            self.use_cache = False
        else:
            self.candidateInfo_list = copy.copy(getCandidateInfoList())
            self.use_cache = True

        if series_uid:
            self.series_list = [series_uid]
        else:
            self.series_list = sorted(set(candidateInfo_tup.series_uid for candidateInfo_tup in self.candidateInfo_list))

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

        series_set = set(self.series_list)
        self.candidateInfo_list = [x for x in self.candidateInfo_list if x.series_uid in series_set]

        if sortby_str == 'random':
            random.shuffle(self.candidateInfo_list)
        elif sortby_str == 'series_uid':
            self.candidateInfo_list.sort(key=lambda x: (x.series_uid, x.center_xyz))
        elif sortby_str == 'label_and_size':
            pass
        else:
            raise Exception("Unknown sort: " + repr(sortby_str))

        self.neg_list = [nt for nt in self.candidateInfo_list if not nt.isNodule_bool]
        self.pos_list = [nt for nt in self.candidateInfo_list if nt.isNodule_bool]
        self.ben_list = [nt for nt in self.pos_list if not nt.isMal_bool]
        self.mal_list = [nt for nt in self.pos_list if nt.isMal_bool]

        log.info("{!r}: {} {} samples, {} neg, {} pos, {} ratio".format(
            self,
            len(self.candidateInfo_list),
            "validation" if isValSet_bool else "training",
            len(self.neg_list),
            len(self.pos_list),
            '{}:1'.format(self.ratio_int) if self.ratio_int else 'unbalanced'
        ))

    def shuffleSamples(self):
        if self.ratio_int:
            random.shuffle(self.candidateInfo_list)
            random.shuffle(self.neg_list)
            random.shuffle(self.pos_list)
            random.shuffle(self.ben_list)
            random.shuffle(self.mal_list)

    def __len__(self):
        if self.ratio_int:
            return 50000
            # return 50000
            # return 200000
        else:
            return len(self.candidateInfo_list)

    def __getitem__(self, ndx):
        if self.ratio_int:
            pos_ndx = ndx // (self.ratio_int + 1)

            if ndx % (self.ratio_int + 1):
                neg_ndx = ndx - 1 - pos_ndx
                neg_ndx %= len(self.neg_list)
                candidateInfo_tup = self.neg_list[neg_ndx]
            else:
                pos_ndx %= len(self.pos_list)
                candidateInfo_tup = self.pos_list[pos_ndx]
        else:
            candidateInfo_tup = self.candidateInfo_list[ndx]

        return self.sampleFromCandidateInfo_tup(candidateInfo_tup, candidateInfo_tup.isNodule_bool)

    def sampleFromCandidateInfo_tup(self, candidateInfo_tup, label_bool):
        width_irc = (32, 48, 48)

        if self.augmentation_dict:
            candidate_t, center_irc = getCtAugmentedCandidate(
                self.augmentation_dict,
                candidateInfo_tup.series_uid,
                candidateInfo_tup.center_xyz,
                width_irc,
                self.use_cache,
            )
        elif self.use_cache:
            candidate_a, center_irc = getCtRawCandidate(
                candidateInfo_tup.series_uid,
                candidateInfo_tup.center_xyz,
                width_irc,
            )
            candidate_t = torch.from_numpy(candidate_a).to(torch.float32)
            candidate_t = candidate_t.unsqueeze(0)
        else:
            ct = getCt(candidateInfo_tup.series_uid)
            candidate_a, center_irc = ct.getRawCandidate(
                candidateInfo_tup.center_xyz,
                width_irc,
            )
            candidate_t = torch.from_numpy(candidate_a).to(torch.float32)
            candidate_t = candidate_t.unsqueeze(0)

        label_t = torch.tensor([False, False], dtype=torch.long)

        if not label_bool:
            label_t[0] = True
            index_t = 0
        else:
            label_t[1] = True
            index_t = 1

        return candidate_t, label_t, index_t, candidateInfo_tup.series_uid, torch.tensor(center_irc)


# class MalignantLunaDataset(LunaDataset):
# # tag::ds_balancing_len[]
#     def __len__(self):
#         if self.ratio_int:
#             return 10000
#             # return 50000
#             # return 200000
#         else:
#             return len(self.ben_list + self.mal_list)
# # end::ds_balancing_len[]
#
# # tag::ds_balancing_getitem[]
#     def __getitem__(self, ndx):
#         if self.ratio_int:
#             mal_ndx = ndx // (self.ratio_int + 1)
#
#             if ndx % (self.ratio_int + 1):
#                 ben_ndx = ndx - 1 - mal_ndx
#                 ben_ndx %= len(self.ben_list)
#                 candidateInfo_tup = self.ben_list[ben_ndx]
#             else:
#                 mal_ndx %= len(self.mal_list)
#                 candidateInfo_tup = self.mal_list[mal_ndx]
#         else:
#             if ndx >= len(self.ben_list):
#                 candidateInfo_tup = self.mal_list[ndx - len(self.ben_list)]
#             else:
#                 candidateInfo_tup = self.ben_list[ndx]
#
#         return self.sampleFromCandidateInfo_tup(candidateInfo_tup, candidateInfo_tup.isMal_bool)
# # end::ds_balancing_getitem[]

class MalignantLunaDataset(LunaDataset):
    def __len__(self):
        if self.ratio_int:
            # return 10000
            return 100000
            # return 50000
            # return 200000
        else:
            return len(self.ben_list + self.mal_list)

    def __getitem__(self, ndx):
        if self.ratio_int:
            if ndx % 2 != 0:
                candidateInfo_tup = self.mal_list[(ndx // 2) % len(self.mal_list)]
            elif ndx % 4 == 0:
                candidateInfo_tup = self.ben_list[(ndx // 4) % len(self.ben_list)]
            else:
                candidateInfo_tup = self.neg_list[(ndx // 4) % len(self.neg_list)]
        else:
            if ndx >= len(self.ben_list):
                candidateInfo_tup = self.mal_list[ndx - len(self.ben_list)]
            else:
                candidateInfo_tup = self.ben_list[ndx]

        return self.sampleFromCandidateInfo_tup(candidateInfo_tup, candidateInfo_tup.isMal_bool)