Deep-Learning-with-PyTorch

INDEX 487
padded convolutions 292
padding 362
padding flag 370
pandas library 41, 78, 377
parallelism 53
parameter estimation 106–109
choosing linear model
108–109
data gathering 107–108
data visualization 108
example problem 107
parameter groups 427
parameters 120, 145, 160, 188,
196, 225, 397
parameters() method
156, 188, 210
params tensor 124, 126, 129
parser.add_argument 352
patient coordinate system
266–267
converting between
millimeters and voxel
addresses 268–270
CT scan shape and voxel
sizes 267–268
extracting nodules from CT
scans 270–271
overview 265–267
penalization terms 134
permute method 73, 170
pickle library 397
pin_memory option 216
points tensor 46, 57, 64
points_gpu tensor 64
pointwise ops 53
pooling 203–204
pos_list 383, 418
pos_ndx 340
pos_t 382
positive loss 344
positive_mask 376
POST route 447
PR (Precision-Recall)
Curves 311
precision 326
implementing in
logMetrics 327–328
updating logging output to
include 332
predict method 209
Predicted Nodules 395, 416
prediction images 393
prediction_a 394
prediction_devtensor 390
prepcache script 376, 440
preprocess function 23
pretext tasks 436
pretrained keyword
argument 36
pretrained networks 423
describing content of
images 33–35
fabricating false images from
real images 27–33
CycleGAN 29–30
GAN game 28
generating images 30–33
recognizing subject of
images 17–27
AlexNet 20–22
inference 25–27
obtaining 19–20
ResNet 22–27
Torch Hub 35–37
principled augmentation 222
Project Gutenberg 94
PyLIDC library 417–418
pyplot.figure 431
Python, list indexing in 42
PyTorch 6
functional API 210–212
how supports deep learning
projects 10–13
keeping track of parameters
and submodules 209–210
reasons for using 7–9
PyTorch JIT 458–465
dual nature of PyTorch as
interface and backend 460
expectations 458–460
scripting gaps of
traceability 464–465
TorchScript 461–464
PyTorch models
enterprise serving of 476
exporting 455–458
ONNX 455–456
tracing 456–458
serving 446–454
Flask server 446–448
goals of deployment
448–449
request batching 449–454
PyTorch Serving 476
pytorch_android library 473
pytorch_android_torchvision
473
Q
quantization 475–476
quantized tensors 65
queue_lock 452
R
random sampling 53
random_float function 349
random.random() function 307
randperm function 134
range indexing 46
ratio_int 339–340
recall 324
implementing in
logMetrics 327–328
updating logging output to
include 332
recurrent neural network 34
RedisAI 476
reduced training 475
reduction ops 53
refine_names method 48
regression problems 107
regularization 219–223
augmentation and 435
batch normalization 222–223
dropout 220–222
weight penalties 219–220
ReLU (rectified linear
unit) 147, 224
rename method 48
request batching 449–454
from request to queue
452–453
implementation 451–452
running batches from
queue 453–454
RequestProcessor 450, 452
requireOnDisk_bool
parameter 260
requires_grad attribute 138
requires_grad–True
argument 124
residual networks 224
ResNet 19, 225
creating network instance 22
details about structure of
22–25
inference 25–27
resnet variable 23
resnet101 function 22
resnet18 function 36
ResNetGenerator class 30