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3D_U_Net_train_URO_config.yml
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3D_U_Net_train_URO_config.yml
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# Training config file
model:
name: UNet3D
# number of input channels to the model
in_channels: 1
# number of output channels
out_channels: 1
# determines the order of operators in a single layer (crg - Conv3d+ReLU+GroupNorm)
layer_order: gcr
# initial number of feature maps
f_maps: [32, 64, 128, 256]
# number of groups in the groupnorm
num_groups: 8
# apply element-wise nn.Sigmoid after the final 1x1x1 convolution, otherwise apply nn.Softmax
final_sigmoid: true
# if True applies the final normalization layer (sigmoid or softmax), otherwise the networks returns the output from the final convolution layer; use False for regression problems, e.g. de-noising
is_segmentation: true
# loss function to be used during training
loss:
name: BCEWithLogitsLoss # BCEWithLogitsLoss, BCEDiceLoss
# skip the last channel in the target (i.e. when last channel contains data not relevant for the loss)
skip_last_target: true
optimizer:
# initial learning rate
learning_rate: 0.0002
# weight decay
weight_decay: 0.00001
# evaluation metric
eval_metric:
# use average precision metric
name: BlobsAveragePrecision
# values on which the nuclei probability maps will be thresholded for AP computation
thresholds: [0.4, 0.5, 0.6, 0.7, 0.8]
metric: 'ap'
lr_scheduler:
# reduce learning rate when evaluation metric plateaus
name: ReduceLROnPlateau
# use 'max' if eval_score_higher_is_better=True, 'min' otherwise
mode: max
# factor by which learning rate will be reduced
factor: 0.2
# number of *validation runs* with no improvement after which learning rate will be reduced
patience: 8
trainer:
# model with lower eval score is considered better
eval_score_higher_is_better: True
# path to the checkpoint directory
checkpoint_dir: /home/yuvi/Desktop/DATASETS_OUTPUTS/Task_3/pytorch_faster_rcnn_stuff/Uroscell/NEW/checkpoint
# path to latest checkpoint; if provided the training will be resumed from that checkpoint
resume: null
# path to the best_checkpoint.pytorch; to be used for fine-tuning the model with additional ground truth
pre_trained: null # /home/yuvi/Desktop/DATASETS_OUTPUTS/Task_3/pytorch_faster_rcnn_stuff/Uroscell/NEW/pre-trained_model/best_checkpoint.pytorch
# how many iterations between validations
validate_after_iters: 100
# how many iterations between tensorboard logging
log_after_iters: 50
# max number of epochs
max_num_epochs: 100
# max number of iterations
max_num_iterations: 1500
# Configure training and validation loaders
loaders:
dataset: StandardHDF5Dataset
batch_size: 1
# how many subprocesses to use for data loading
num_workers: 1
# path to the raw data within the H5
raw_internal_path: /raw
# path to the the label data withtin the H5
label_internal_path: /label
# path to the pixel-wise weight map withing the H5 if present
weight_internal_path: null
# configuration of the train loader
train:
# path to the training datasets
file_paths:
- - /..../train_h5 # Pass the training file in .h5
# SliceBuilder configuration, i.e. how to iterate over the input volume patch-by-patch
slice_builder:
name: FilterSliceBuilder
# train patch size given to the network (adapt to fit in your GPU mem, generally the bigger patch the better)
patch_shape: [64, 64, 64]
# train stride between patches
stride_shape: [20, 40, 40]
# minimum volume of the labels in the patch
threshold: 0.01
# probability of accepting patches which do not fulfil the threshold criterion
slack_acceptance: 0.01
transformer:
raw:
- name: Standardize
- name: RandomFlip
- name: RandomRotate90
- name: RandomRotate
# rotate only in ZY plane due to anisotropy
axes: [[2, 1]]
angle_spectrum: 30
mode: reflect
- name: ElasticDeformation
spline_order: 3
- name: ToTensor
expand_dims: true
label:
- name: RandomFlip
- name: RandomRotate90
- name: RandomRotate
# rotate only in ZY plane due to anisotropy
axes: [[2, 1]]
angle_spectrum: 30
mode: reflect
- name: ElasticDeformation
spline_order: 0
# convert target volume to binary mask
- name: BlobsToMask
# append ground truth labels in the last channel of the target for evaluation metric computation
append_label: true
# if 'true' appends boundary mask as a 2nd channel of the target; boundaries are computed using the 'find_boundaries()' function from skimage
# learning the boundaries as a 2nd objective sometimes helps with the nuclei mask prediction
boundary: false
- name: ToTensor
expand_dims: false
# configuration of the val loader
val:
# path to the val datasets
file_paths:
- /..../valid_h5 # Pass the validaion file in .h5
# SliceBuilder configuration, i.e. how to iterate over the input volume patch-by-patch
slice_builder:
name: FilterSliceBuilder
# train patch size given to the network (adapt to fit in your GPU mem, generally the bigger patch the better)
patch_shape: [64, 64, 64]
# train stride between patches
stride_shape: [64, 64, 64]
# minimum volume of the labels in the patch
threshold: 0.01
# probability of accepting patches which do not fulfil the threshold criterion
slack_acceptance: 0.01
# data augmentation
transformer:
raw:
- name: Standardize
- name: ToTensor
expand_dims: true
label:
- name: BlobsToMask
append_label: true
boundary: false
- name: ToTensor
expand_dims: false