diff --git a/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/__init__.py b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/__init__.py
new file mode 100644
index 00000000..06d74050
Binary files /dev/null and b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/__init__.py differ
diff --git a/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/benchmark_seg.py b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/benchmark_seg.py
new file mode 100644
index 00000000..05384204
--- /dev/null
+++ b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/benchmark_seg.py
@@ -0,0 +1,76 @@
+import argparse
+import datetime
+import os
+import socket
+import sys
+
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+import torch
+import torch.nn as nn
+import torch.optim
+
+from torch.optim import SGD, Adam
+from torch.utils.data import DataLoader
+
+from util.util import enumerateWithEstimate
+from p2ch13.dsets import Luna2dSegmentationDataset, TrainingLuna2dSegmentationDataset, getCt
+from util.logconf import logging
+from util.util import xyz2irc
+from p2ch13.model_seg import UNetWrapper, SegmentationAugmentation
+from p2ch13.train_seg import LunaTrainingApp
+
+log = logging.getLogger(__name__)
+# log.setLevel(logging.WARN)
+# log.setLevel(logging.INFO)
+log.setLevel(logging.DEBUG)
+
+class BenchmarkLuna2dSegmentationDataset(TrainingLuna2dSegmentationDataset):
+    def __len__(self):
+        # return 500
+        return 5000
+        return 1000
+
+class LunaBenchmarkApp(LunaTrainingApp):
+    def initTrainDl(self):
+        train_ds = BenchmarkLuna2dSegmentationDataset(
+            val_stride=10,
+            isValSet_bool=False,
+            contextSlices_count=3,
+            # augmentation_dict=self.augmentation_dict,
+        )
+
+        batch_size = self.cli_args.batch_size
+        if self.use_cuda:
+            batch_size *= torch.cuda.device_count()
+
+        train_dl = DataLoader(
+            train_ds,
+            batch_size=batch_size,
+            num_workers=self.cli_args.num_workers,
+            pin_memory=self.use_cuda,
+        )
+
+        return train_dl
+
+    def main(self):
+        log.info("Starting {}, {}".format(type(self).__name__, self.cli_args))
+
+        train_dl = self.initTrainDl()
+
+        for epoch_ndx in range(1, 2):
+            log.info("Epoch {} of {}, {}/{} batches of size {}*{}".format(
+                epoch_ndx,
+                self.cli_args.epochs,
+                len(train_dl),
+                len([]),
+                self.cli_args.batch_size,
+                (torch.cuda.device_count() if self.use_cuda else 1),
+            ))
+
+            self.doTraining(epoch_ndx, train_dl)
+
+
+if __name__ == '__main__':
+    LunaBenchmarkApp().main()
diff --git a/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/dsets.py b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/dsets.py
new file mode 100644
index 00000000..f16b1386
--- /dev/null
+++ b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/dsets.py
@@ -0,0 +1,401 @@
+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 scipy.ndimage.morphology as morph
+
+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('part2ch13_raw')
+
+MaskTuple = namedtuple('MaskTuple', 'raw_dense_mask, dense_mask, body_mask, air_mask, raw_candidate_mask, candidate_mask, lung_mask, neg_mask, pos_mask')
+
+CandidateInfoTuple = namedtuple('CandidateInfoTuple', 'isNodule_bool, hasAnnotation_bool, isMal_bool, diameter_mm, series_uid, center_xyz')
+
+@functools.lru_cache(1)
+def getCandidateInfoList(requireOnDisk_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/subset*/*.mhd')
+    presentOnDisk_set = {os.path.split(p)[-1][:-4] for p in mhd_list}
+
+    candidateInfo_list = []
+    with open('data/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/candidates.csv', "r") as f:
+        for row in list(csv.reader(f))[1:]:
+            series_uid = row[0]
+
+            if series_uid not in presentOnDisk_set and requireOnDisk_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(requireOnDisk_bool=True):
+    candidateInfo_list = getCandidateInfoList(requireOnDisk_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/subset*/{}.mhd'.format(series_uid)
+        )[0]
+
+        ct_mhd = sitk.ReadImage(mhd_path)
+        self.hu_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.
+
+        self.series_uid = series_uid
+
+        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)
+
+        candidateInfo_list = getCandidateInfoDict()[self.series_uid]
+
+        self.positiveInfo_list = [
+            candidate_tup
+            for candidate_tup in candidateInfo_list
+            if candidate_tup.isNodule_bool
+        ]
+        self.positive_mask = self.buildAnnotationMask(self.positiveInfo_list)
+        self.positive_indexes = (self.positive_mask.sum(axis=(1,2))
+                                 .nonzero()[0].tolist())
+
+    def buildAnnotationMask(self, positiveInfo_list, threshold_hu = -700):
+        boundingBox_a = np.zeros_like(self.hu_a, dtype=np.bool)
+
+        for candidateInfo_tup in positiveInfo_list:
+            center_irc = xyz2irc(
+                candidateInfo_tup.center_xyz,
+                self.origin_xyz,
+                self.vxSize_xyz,
+                self.direction_a,
+            )
+            ci = int(center_irc.index)
+            cr = int(center_irc.row)
+            cc = int(center_irc.col)
+
+            index_radius = 2
+            try:
+                while self.hu_a[ci + index_radius, cr, cc] > threshold_hu and \
+                        self.hu_a[ci - index_radius, cr, cc] > threshold_hu:
+                    index_radius += 1
+            except IndexError:
+                index_radius -= 1
+
+            row_radius = 2
+            try:
+                while self.hu_a[ci, cr + row_radius, cc] > threshold_hu and \
+                        self.hu_a[ci, cr - row_radius, cc] > threshold_hu:
+                    row_radius += 1
+            except IndexError:
+                row_radius -= 1
+
+            col_radius = 2
+            try:
+                while self.hu_a[ci, cr, cc + col_radius] > threshold_hu and \
+                        self.hu_a[ci, cr, cc - col_radius] > threshold_hu:
+                    col_radius += 1
+            except IndexError:
+                col_radius -= 1
+
+            # assert index_radius > 0, repr([candidateInfo_tup.center_xyz, center_irc, self.hu_a[ci, cr, cc]])
+            # assert row_radius > 0
+            # assert col_radius > 0
+
+            boundingBox_a[
+                 ci - index_radius: ci + index_radius + 1,
+                 cr - row_radius: cr + row_radius + 1,
+                 cc - col_radius: cc + col_radius + 1] = True
+
+        mask_a = boundingBox_a & (self.hu_a > threshold_hu)
+
+        return mask_a
+
+    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)]
+        pos_chunk = self.positive_mask[tuple(slice_list)]
+
+        return ct_chunk, pos_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, pos_chunk, center_irc = ct.getRawCandidate(center_xyz,
+                                                         width_irc)
+    ct_chunk.clip(-1000, 1000, ct_chunk)
+    return ct_chunk, pos_chunk, center_irc
+
+@raw_cache.memoize(typed=True)
+def getCtSampleSize(series_uid):
+    ct = Ct(series_uid)
+    return int(ct.hu_a.shape[0]), ct.positive_indexes
+
+
+class Luna2dSegmentationDataset(Dataset):
+    def __init__(self,
+                 val_stride=0,
+                 isValSet_bool=None,
+                 series_uid=None,
+                 contextSlices_count=3,
+                 fullCt_bool=False,
+            ):
+        self.contextSlices_count = contextSlices_count
+        self.fullCt_bool = fullCt_bool
+
+        if series_uid:
+            self.series_list = [series_uid]
+        else:
+            self.series_list = sorted(getCandidateInfoDict().keys())
+
+        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
+
+        self.sample_list = []
+        for series_uid in self.series_list:
+            index_count, positive_indexes = getCtSampleSize(series_uid)
+
+            if self.fullCt_bool:
+                self.sample_list += [(series_uid, slice_ndx)
+                                     for slice_ndx in range(index_count)]
+            else:
+                self.sample_list += [(series_uid, slice_ndx)
+                                     for slice_ndx in positive_indexes]
+
+        self.candidateInfo_list = getCandidateInfoList()
+
+        series_set = set(self.series_list)
+        self.candidateInfo_list = [cit for cit in self.candidateInfo_list
+                                   if cit.series_uid in series_set]
+
+        self.pos_list = [nt for nt in self.candidateInfo_list
+                            if nt.isNodule_bool]
+
+        log.info("{!r}: {} {} series, {} slices, {} nodules".format(
+            self,
+            len(self.series_list),
+            {None: 'general', True: 'validation', False: 'training'}[isValSet_bool],
+            len(self.sample_list),
+            len(self.pos_list),
+        ))
+
+    def __len__(self):
+        return len(self.sample_list)
+
+    def __getitem__(self, ndx):
+        series_uid, slice_ndx = self.sample_list[ndx % len(self.sample_list)]
+        return self.getitem_fullSlice(series_uid, slice_ndx)
+
+    def getitem_fullSlice(self, series_uid, slice_ndx):
+        ct = getCt(series_uid)
+        ct_t = torch.zeros((self.contextSlices_count * 2 + 1, 512, 512))
+
+        start_ndx = slice_ndx - self.contextSlices_count
+        end_ndx = slice_ndx + self.contextSlices_count + 1
+        for i, context_ndx in enumerate(range(start_ndx, end_ndx)):
+            context_ndx = max(context_ndx, 0)
+            context_ndx = min(context_ndx, ct.hu_a.shape[0] - 1)
+            ct_t[i] = torch.from_numpy(ct.hu_a[context_ndx].astype(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_t.clamp_(-1000, 1000)
+
+        pos_t = torch.from_numpy(ct.positive_mask[slice_ndx]).unsqueeze(0)
+
+        return ct_t, pos_t, ct.series_uid, slice_ndx
+
+
+class TrainingLuna2dSegmentationDataset(Luna2dSegmentationDataset):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.ratio_int = 2
+
+    def __len__(self):
+        return 300000
+
+    def shuffleSamples(self):
+        random.shuffle(self.candidateInfo_list)
+        random.shuffle(self.pos_list)
+
+    def __getitem__(self, ndx):
+        candidateInfo_tup = self.pos_list[ndx % len(self.pos_list)]
+        return self.getitem_trainingCrop(candidateInfo_tup)
+
+    def getitem_trainingCrop(self, candidateInfo_tup):
+        ct_a, pos_a, center_irc = getCtRawCandidate(
+            candidateInfo_tup.series_uid,
+            candidateInfo_tup.center_xyz,
+            (7, 96, 96),
+        )
+        pos_a = pos_a[3:4]
+
+        row_offset = random.randrange(0,32)
+        col_offset = random.randrange(0,32)
+        ct_t = torch.from_numpy(ct_a[:, row_offset:row_offset+64,
+                                     col_offset:col_offset+64]).to(torch.float32)
+        pos_t = torch.from_numpy(pos_a[:, row_offset:row_offset+64,
+                                       col_offset:col_offset+64]).to(torch.long)
+
+        slice_ndx = center_irc.index
+
+        return ct_t, pos_t, candidateInfo_tup.series_uid, slice_ndx
+
+class PrepcacheLunaDataset(Dataset):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.candidateInfo_list = getCandidateInfoList()
+        self.pos_list = [nt for nt in self.candidateInfo_list if nt.isNodule_bool]
+
+        self.seen_set = set()
+        self.candidateInfo_list.sort(key=lambda x: x.series_uid)
+
+    def __len__(self):
+        return len(self.candidateInfo_list)
+
+    def __getitem__(self, ndx):
+        # candidate_t, pos_t, series_uid, center_t = super().__getitem__(ndx)
+
+        candidateInfo_tup = self.candidateInfo_list[ndx]
+        getCtRawCandidate(candidateInfo_tup.series_uid, candidateInfo_tup.center_xyz, (7, 96, 96))
+
+        series_uid = candidateInfo_tup.series_uid
+        if series_uid not in self.seen_set:
+            self.seen_set.add(series_uid)
+
+            getCtSampleSize(series_uid)
+            # ct = getCt(series_uid)
+            # for mask_ndx in ct.positive_indexes:
+            #     build2dLungMask(series_uid, mask_ndx)
+
+        return 0, 1 #candidate_t, pos_t, series_uid, center_t
+
+
+class TvTrainingLuna2dSegmentationDataset(torch.utils.data.Dataset):
+    def __init__(self, isValSet_bool=False, val_stride=10, contextSlices_count=3):
+        assert contextSlices_count == 3
+        data = torch.load('./imgs_and_masks.pt')
+        suids = list(set(data['suids']))
+        trn_mask_suids = torch.arange(len(suids)) % val_stride < (val_stride - 1)
+        trn_suids = {s for i, s in zip(trn_mask_suids, suids) if i}
+        trn_mask = torch.tensor([(s in trn_suids) for s in data["suids"]])
+        if not isValSet_bool:
+            self.imgs = data["imgs"][trn_mask]
+            self.masks = data["masks"][trn_mask]
+            self.suids = [s for s, i in zip(data["suids"], trn_mask) if i]
+        else:
+            self.imgs = data["imgs"][~trn_mask]
+            self.masks = data["masks"][~trn_mask]
+            self.suids = [s for s, i in zip(data["suids"], trn_mask) if not i]
+        # discard spurious hotspots and clamp bone
+        self.imgs.clamp_(-1000, 1000)
+        self.imgs /= 1000
+
+
+    def __len__(self):
+        return len(self.imgs)
+
+    def __getitem__(self, i):
+        oh, ow = torch.randint(0, 32, (2,))
+        sl = self.masks.size(1)//2
+        return self.imgs[i, :, oh: oh + 64, ow: ow + 64], 1, self.masks[i, sl: sl+1, oh: oh + 64, ow: ow + 64].to(torch.float32), self.suids[i], 9999
diff --git a/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/model.py b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/model.py
new file mode 100644
index 00000000..20cecbb9
--- /dev/null
+++ b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/model.py
@@ -0,0 +1,224 @@
+import math
+import random
+from collections import namedtuple
+
+import torch
+from torch import nn as nn
+import torch.nn.functional as F
+
+from util.logconf import logging
+from util.unet import UNet
+
+log = logging.getLogger(__name__)
+# log.setLevel(logging.WARN)
+# log.setLevel(logging.INFO)
+log.setLevel(logging.DEBUG)
+
+class UNetWrapper(nn.Module):
+    def __init__(self, **kwargs):
+        super().__init__()
+
+        self.input_batchnorm = nn.BatchNorm2d(kwargs['in_channels'])
+        self.unet = UNet(**kwargs)
+        self.final = nn.Sigmoid()
+
+        self._init_weights()
+
+    def _init_weights(self):
+        init_set = {
+            nn.Conv2d,
+            nn.Conv3d,
+            nn.ConvTranspose2d,
+            nn.ConvTranspose3d,
+            nn.Linear,
+        }
+        for m in self.modules():
+            if type(m) in init_set:
+                nn.init.kaiming_normal_(
+                    m.weight.data, mode='fan_out', nonlinearity='relu', a=0
+                )
+                if m.bias is not None:
+                    fan_in, fan_out = \
+                        nn.init._calculate_fan_in_and_fan_out(m.weight.data)
+                    bound = 1 / math.sqrt(fan_out)
+                    nn.init.normal_(m.bias, -bound, bound)
+
+        # nn.init.constant_(self.unet.last.bias, -4)
+        # nn.init.constant_(self.unet.last.bias, 4)
+
+
+    def forward(self, input_batch):
+        bn_output = self.input_batchnorm(input_batch)
+        un_output = self.unet(bn_output)
+        fn_output = self.final(un_output)
+        return fn_output
+
+class SegmentationAugmentation(nn.Module):
+    def __init__(
+            self, flip=None, offset=None, scale=None, rotate=None, noise=None
+    ):
+        super().__init__()
+
+        self.flip = flip
+        self.offset = offset
+        self.scale = scale
+        self.rotate = rotate
+        self.noise = noise
+
+    def forward(self, input_g, label_g):
+        transform_t = self._build2dTransformMatrix()
+        transform_t = transform_t.expand(input_g.shape[0], -1, -1)
+        transform_t = transform_t.to(input_g.device, torch.float32)
+        affine_t = F.affine_grid(transform_t[:,:2],
+                input_g.size(), align_corners=False)
+
+        augmented_input_g = F.grid_sample(input_g,
+                affine_t, padding_mode='border',
+                align_corners=False)
+        augmented_label_g = F.grid_sample(label_g.to(torch.float32),
+                affine_t, padding_mode='border',
+                align_corners=False)
+
+        if self.noise:
+            noise_t = torch.randn_like(augmented_input_g)
+            noise_t *= self.noise
+
+            augmented_input_g += noise_t
+
+        return augmented_input_g, augmented_label_g > 0.5
+
+    def _build2dTransformMatrix(self):
+        transform_t = torch.eye(3)
+
+        for i in range(2):
+            if self.flip:
+                if random.random() > 0.5:
+                    transform_t[i,i] *= -1
+
+            if self.offset:
+                offset_float = self.offset
+                random_float = (random.random() * 2 - 1)
+                transform_t[2,i] = offset_float * random_float
+
+            if self.scale:
+                scale_float = self.scale
+                random_float = (random.random() * 2 - 1)
+                transform_t[i,i] *= 1.0 + scale_float * random_float
+
+        if self.rotate:
+            angle_rad = random.random() * math.pi * 2
+            s = math.sin(angle_rad)
+            c = math.cos(angle_rad)
+
+            rotation_t = torch.tensor([
+                [c, -s, 0],
+                [s, c, 0],
+                [0, 0, 1]])
+
+            transform_t @= rotation_t
+
+        return transform_t
+
+
+# MaskTuple = namedtuple('MaskTuple', 'raw_dense_mask, dense_mask, body_mask, air_mask, raw_candidate_mask, candidate_mask, lung_mask, neg_mask, pos_mask')
+#
+# class SegmentationMask(nn.Module):
+#     def __init__(self):
+#         super().__init__()
+#
+#         self.conv_list = nn.ModuleList([
+#             self._make_circle_conv(radius) for radius in range(1, 8)
+#         ])
+#
+#     def _make_circle_conv(self, radius):
+#         diameter = 1 + radius * 2
+#
+#         a = torch.linspace(-1, 1, steps=diameter)**2
+#         b = (a[None] + a[:, None])**0.5
+#
+#         circle_weights = (b <= 1.0).to(torch.float32)
+#
+#         conv = nn.Conv2d(1, 1, kernel_size=diameter, padding=radius, bias=False)
+#         conv.weight.data.fill_(1)
+#         conv.weight.data *= circle_weights / circle_weights.sum()
+#
+#         return conv
+#
+#
+#     def erode(self, input_mask, radius, threshold=1):
+#         conv = self.conv_list[radius - 1]
+#         input_float = input_mask.to(torch.float32)
+#         result = conv(input_float)
+#
+#         # log.debug(['erode in ', radius, threshold, input_float.min().item(), input_float.mean().item(), input_float.max().item()])
+#         # log.debug(['erode out', radius, threshold, result.min().item(), result.mean().item(), result.max().item()])
+#
+#         return result >= threshold
+#
+#     def deposit(self, input_mask, radius, threshold=0):
+#         conv = self.conv_list[radius - 1]
+#         input_float = input_mask.to(torch.float32)
+#         result = conv(input_float)
+#
+#         # log.debug(['deposit in ', radius, threshold, input_float.min().item(), input_float.mean().item(), input_float.max().item()])
+#         # log.debug(['deposit out', radius, threshold, result.min().item(), result.mean().item(), result.max().item()])
+#
+#         return result > threshold
+#
+#     def fill_cavity(self, input_mask):
+#         cumsum = input_mask.cumsum(-1)
+#         filled_mask = (cumsum > 0)
+#         filled_mask &= (cumsum < cumsum[..., -1:])
+#         cumsum = input_mask.cumsum(-2)
+#         filled_mask &= (cumsum > 0)
+#         filled_mask &= (cumsum < cumsum[..., -1:, :])
+#
+#         return filled_mask
+#
+#
+#     def forward(self, input_g, raw_pos_g):
+#         gcc_g = input_g + 1
+#
+#         with torch.no_grad():
+#             # log.info(['gcc_g', gcc_g.min(), gcc_g.mean(), gcc_g.max()])
+#
+#             raw_dense_mask = gcc_g > 0.7
+#             dense_mask = self.deposit(raw_dense_mask, 2)
+#             dense_mask = self.erode(dense_mask, 6)
+#             dense_mask = self.deposit(dense_mask, 4)
+#
+#             body_mask = self.fill_cavity(dense_mask)
+#             air_mask = self.deposit(body_mask & ~dense_mask, 5)
+#             air_mask = self.erode(air_mask, 6)
+#
+#             lung_mask = self.deposit(air_mask, 5)
+#
+#             raw_candidate_mask = gcc_g > 0.4
+#             raw_candidate_mask &= air_mask
+#             candidate_mask = self.erode(raw_candidate_mask, 1)
+#             candidate_mask = self.deposit(candidate_mask, 1)
+#
+#             pos_mask = self.deposit((raw_pos_g > 0.5) & lung_mask, 2)
+#
+#             neg_mask = self.deposit(candidate_mask, 1)
+#             neg_mask &= ~pos_mask
+#             neg_mask &= lung_mask
+#
+#             # label_g = (neg_mask | pos_mask).to(torch.float32)
+#             label_g = (pos_mask).to(torch.float32)
+#             neg_g = neg_mask.to(torch.float32)
+#             pos_g = pos_mask.to(torch.float32)
+#
+#         mask_dict = {
+#             'raw_dense_mask': raw_dense_mask,
+#             'dense_mask': dense_mask,
+#             'body_mask': body_mask,
+#             'air_mask': air_mask,
+#             'raw_candidate_mask': raw_candidate_mask,
+#             'candidate_mask': candidate_mask,
+#             'lung_mask': lung_mask,
+#             'neg_mask': neg_mask,
+#             'pos_mask': pos_mask,
+#         }
+#
+#         return label_g, neg_g, pos_g, lung_mask, mask_dict
diff --git a/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/prepcache.py b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/prepcache.py
new file mode 100644
index 00000000..9e867cde
--- /dev/null
+++ b/pages/students/2016/lukas_pokryvka/dp2021/lungCancer/model/prepcache.py
@@ -0,0 +1,69 @@
+import timing
+import argparse
+import sys
+
+import numpy as np
+
+import torch.nn as nn
+from torch.autograd import Variable
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+
+from util.util import enumerateWithEstimate
+from .dsets import PrepcacheLunaDataset, getCtSampleSize
+from util.logconf import logging
+# from .model import LunaModel
+
+log = logging.getLogger(__name__)
+# log.setLevel(logging.WARN)
+log.setLevel(logging.INFO)
+# log.setLevel(logging.DEBUG)
+
+
+class LunaPrepCacheApp:
+    @classmethod
+    def __init__(self, sys_argv=None):
+        if sys_argv is None:
+            sys_argv = sys.argv[1:]
+
+        parser = argparse.ArgumentParser()
+        parser.add_argument('--batch-size',
+            help='Batch size to use for training',
+            default=1024,
+            type=int,
+        )
+        parser.add_argument('--num-workers',
+            help='Number of worker processes for background data loading',
+            default=8,
+            type=int,
+        )
+        # parser.add_argument('--scaled',
+        #     help="Scale the CT chunks to square voxels.",
+        #     default=False,
+        #     action='store_true',
+        # )
+
+        self.cli_args = parser.parse_args(sys_argv)
+
+    def main(self):
+        log.info("Starting {}, {}".format(type(self).__name__, self.cli_args))
+
+        self.prep_dl = DataLoader(
+            PrepcacheLunaDataset(
+                # sortby_str='series_uid',
+            ),
+            batch_size=self.cli_args.batch_size,
+            num_workers=self.cli_args.num_workers,
+        )
+
+        batch_iter = enumerateWithEstimate(
+            self.prep_dl,
+            "Stuffing cache",
+            start_ndx=self.prep_dl.num_workers,
+        )
+        for batch_ndx, batch_tup in batch_iter:
+            pass
+
+
+if __name__ == '__main__':
+    LunaPrepCacheApp().main()