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train_config.yml
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train_config.yml
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# Sample configuration file for training a 3D U-Net on a task of predicting the boundaries in 3D stack of the Arabidopsis
# ovules acquired with the confocal microscope. Training done with a combination of Binary Cross-Entropy and DiceLoss.
# Download train data from: https://osf.io/x9yns/
# Download validation data from: https://osf.io/xp5uf/
# Download test data from: https://osf.io/8jz7e/
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
# 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
# loss function to be used during training
loss:
name: BCEDiceLoss
# a target value that is ignored and does not contribute to the input gradient
ignore_index: null
# 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 AdaptedRandError metric
name: BoundaryAdaptedRandError
# probability maps threshold
threshold: 0.4
# use the last target channel to compute the metric
use_last_target: true
# use only the first channel for computing the metric
use_first_input: true
lr_scheduler:
name: ReduceLROnPlateau
# make sure to use the 'min' mode cause lower AdaptedRandError is better
mode: min
factor: 0.5
patience: 30
trainer:
# model with lower eval score is considered better
eval_score_higher_is_better: False
# path to the checkpoint directory
checkpoint_dir: CHECKPOINT_DIR
# 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
# how many iterations between validations
validate_after_iters: 1000
# how many iterations between tensorboard logging
log_after_iters: 500
# max number of epochs
max_num_epochs: 1000
# max number of iterations
max_num_iterations: 150000
# Configure training and validation loaders
loaders:
# how many subprocesses to use for data loading
num_workers: 8
# 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
# configuration of the train loader
train:
# path to the training datasets
file_paths:
- PATH_TO_TRAIN_DIR
# 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: [80, 170, 170]
# train stride between patches
stride_shape: [20, 40, 40]
# minimum volume of the labels in the patch
threshold: 0.6
# 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: 45
mode: reflect
- name: ElasticDeformation
spline_order: 3
- name: GaussianBlur3D
execution_probability: 0.5
- name: AdditiveGaussianNoise
execution_probability: 0.2
- name: AdditivePoissonNoise
execution_probability: 0.2
- 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: 45
mode: reflect
- name: ElasticDeformation
spline_order: 0
- name: StandardLabelToBoundary
# append original ground truth labels to the last channel (to be able to compute the eval metric)
append_label: true
- name: ToTensor
expand_dims: false
# configuration of the val loader
val:
# path to the val datasets
file_paths:
- PATH_TO_VAL_DIR
# 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: [80, 170, 170]
# train stride between patches
stride_shape: [80, 170, 170]
# minimum volume of the labels in the patch
threshold: 0.6
# 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: StandardLabelToBoundary
append_label: true
- name: ToTensor
expand_dims: false