Deep-Learning-with-PyTorch

INDEX 481
classification model training
(continued)
outputting performance
metrics 300–304
constructing masks
302–304
logMetrics function
301–304
pretraining setup and
initialization 284–289
care and feeding of data
loaders 287–289
initializing model and
optimizer 285–287
running training script
304–307
enumerateWithEstimate
function 306–307
needed data for
training 305–306
training and validating the
model 295–300
computeBatchLoss
function 297–299
validation loop 299–300
classification threshold 323
classificationThreshold 302
classificationThreshold_float
324
classifyCandidates method 410
clean_a 411
clean_words tensor 96
clear() method 452
CMake 468
CMakeLists.txt 472
Coco 166
col_radius 373
comparison ops 53
Complete Miss 416
computeBatchLoss
function 297–300, 390, 392
ConcatDataset 174
contextSlices_count
parameter 380
contiguous block 467
contiguous method 61
contiguous tensors 60
continuous values 80
contrastive learning 437
conv.weight 198
conv.weight.one_() method 200
convolutional layers 370
convolutions 194–229
birds vs. airplanes
challenge 196–207
as nn module 208–209
detecting features 200–202
downsampling 203–204
padding boundary 198–200
pooling 203–204
function of 194–196
model design 217–229
comparing designs
228–229
depth of network 223–228
outdated 229
regularization 219–223
width of network 218–219
subclassing nn module
207–212
training 212–217
measuring accuracy 214
on GPU 215–217
saving and loading 214–215
ConvTransposed2d 469
copying 449
coroutines 449
Cortex 476
cost function 109
cpu method 64
create_dataset function 67
creation ops 53
CrossEntropyLoss 297
csv module 78
CT (computed tomography)
scans 75, 240
Ct class 256, 262, 264, 271,
280, 289
CT scans 238–241
bridging CT segmentation
and nodule candidate
classification 408–416
classification to reduce false
positives 412–416
grouping voxels into
nodule candidates
411–412
segmentation 410–411
caching chunks of mask in
addition to CT 376
calling mask creation during
CT initialization 375
extracting nodules from
270–271
Hounsfield units 264–265
loading individual 262–265
scan shape and voxel
sizes 267–268
ct_a values 264
ct_chunk function 348
ct_mhd.GetDirections()
method 268
ct_mhd.GetSpacing()
method 268
ct_ndx 381
ct_t 382, 394
Ct.buildAnnotationMask 374
Ct.getRawCandidate
function 274, 376
ct.positive_mask 383
cuda method 64
cuDNN library 460
CycleGAN 29–30, 452, 458,
464, 468
cyclegan_jit.cpp source file 468
cyclegan-cpp-api 472
D
daily_bikes tensor 90, 92
data augmentation 346–354
improvement from 352–354
on GPU 384–386
techniques 347–352
mirroring 348–349
noise 350
rotating 350
scaling 349
shifting 349
data augmentation strategy 191
data loading
loading individual CT
scans 262–265
locating nodules 265–271
converting between millimeters
and voxel
addresses 268–270
CT scan shape and voxel
sizes 267–268
extracting nodules from CT
scans 270–271
patient coordinate
system 265–267
parsing LUNA's annotation
data 256–262
training and validation
sets 258–259
unifying annotation and
candidate data 259–
262
raw CT data files 256
straightforward dataset
implementation 271–277
caching candidate
arrays 274