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unet2d_baseline.cfg
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unet2d_baseline.cfg
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[dataset]
# tensor type (float or double)
tensor_type = float
task_type = seg
supervise_type = fully_sup
root_dir = ../../PyMIC_data/ACDC/preprocess/
train_csv = config/data/image_train_r10_lab.csv
valid_csv = config/data/image_valid.csv
test_csv = config/data/image_test.csv
train_batch_size = 4
# data transforms
train_transform = [Pad, RandomRotate, RandomCrop, RandomFlip, NormalizeWithMeanStd, GammaCorrection, GaussianNoise, LabelToProbability]
valid_transform = [NormalizeWithMeanStd, Pad, LabelToProbability]
test_transform = [NormalizeWithMeanStd, Pad]
Pad_output_size = [8, 256, 256]
Pad_ceil_mode = False
RandomRotate_angle_range_d = [-90, 90]
RandomRotate_angle_range_h = None
RandomRotate_angle_range_w = None
RandomCrop_output_size = [6, 192, 192]
RandomCrop_foreground_focus = False
RandomCrop_foreground_ratio = None
Randomcrop_mask_label = None
RandomFlip_flip_depth = False
RandomFlip_flip_height = True
RandomFlip_flip_width = True
NormalizeWithMeanStd_channels = [0]
GammaCorrection_channels = [0]
GammaCorrection_gamma_min = 0.7
GammaCorrection_gamma_max = 1.5
GaussianNoise_channels = [0]
GaussianNoise_mean = 0
GaussianNoise_std = 0.05
[network]
# this section gives parameters for network
# the keys may be different for different networks
# type of network
net_type = UNet2D
# number of class, required for segmentation task
class_num = 4
in_chns = 1
feature_chns = [16, 32, 64, 128, 256]
dropout = [0.0, 0.0, 0.0, 0.5, 0.5]
bilinear = True
multiscale_pred = False
[training]
# list of gpus
gpus = [1]
loss_type = [DiceLoss, CrossEntropyLoss]
loss_weight = [0.5, 0.5]
# for optimizers
optimizer = Adam
learning_rate = 1e-3
momentum = 0.9
weight_decay = 1e-5
# for lr schedular
lr_scheduler = ReduceLROnPlateau
lr_gamma = 0.5
ReduceLROnPlateau_patience = 4000
early_stop_patience = 10000
ckpt_save_dir = model/unet2d_baseline
iter_max = 30000
iter_valid = 100
iter_save = 30000
[testing]
# list of gpus
gpus = [0]
# checkpoint mode can be [0-latest, 1-best, 2-specified]
ckpt_mode = 1
output_dir = result/unet2d_baseline
post_process = KeepLargestComponent
sliding_window_enable = True
sliding_window_size = [6, 192, 192]
sliding_window_stride = [6, 192, 192]