class Config(ParentConfig):
train_out_dir = ParentConfig.model_outdir + '/0036_3x3_pretrained_stage2'
train_dataset_file = '5fold-test.csv'
val_dataset_file = '5fold-test.csv'
test_dataset_file = 'test2.csv'
data_version = '3d' # '3d', 'npy', 'npy256' etc.
use_cq500 = False
gpus = [0]
val_folds = [0]
train_folds = get_train_folds(val_folds)
folds_str = '/fold' + get_val_folds_str(val_folds)
train_out_dir += folds_str
backbone = 'resnet34'
# 'imagenet', None or path to weights
# pretrained = 'imagenet'
pretrained = ParentConfig.model_outdir + f'/0034_resnet34_3c/{folds_str}/models/*'
pretrained = sorted(glob.glob(pretrained))[-1]
class Config(BaseConfig):
train_out_dir = BaseConfig.model_outdir + '/0034_resnet34_3c'
train_dataset_file = '5fold.csv'
val_dataset_file = '5fold.csv'
test_dataset_file = 'test.csv'
data_version = '3d' # '3d', 'npy', 'npy256' etc.
use_cq500 = False
val_folds = [0]
train_folds = get_train_folds(val_folds)
train_out_dir += '/fold' + get_val_folds_str(val_folds)
# backbone = 'se_resnext50'
backbone = 'resnet34'
# 'imagenet', None or path to weights
pretrained = 'imagenet'
# pretrained = '/kolos/m2/ct/models/classification/rsna/0014_384/0123/models/_ckpt_epoch_2.ckpt'
lr = 1e-4
batch_size = 64 # 16 (3, 512, 512) images fits on TITAN XP
accumulate_grad_batches = 1
dropout = 0.5
weight_decay = 0.001
optimizer = 'radam'
scheduler = {
'name': 'flat_anneal',
'flat_iterations': 8000,
'anneal_iterations': 12000,
'min_lr': 1e-7
}
# scheduler = {
# 'name': 'LambdaLR',
# 'iter_to_lr': {
# 0: 1e-3,
# 4000: 1e-4,
# 14000: 2e-5,
# 24000: 4e-6
# },
# }
freeze_backbone_iterations = 2000
freeze_first_layer = True
gpus = [0]
num_workers = 3 * len(gpus)
max_epoch = 20
num_slices = 3 # must be odd
pre_crop_size = 400
crop_size = 384
padded_size = None
shift_limit = 0
random_crop = True
vertical_flip = False
pixel_augment = False
elastic_transform = False
use_cdf = True
augment = True
# used only if use_cdf is False
min_hu_value = 20
max_hu_value = 100
balancing = False
# 'epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid', 'subdural', no_bleeding
probas = [0.1, 0.14, 0.14, 0.14, 0.14, 0.34]
multibranch = False
# use 3d conv to merge features from different branches
multibranch3d = False
multibranch_embedding = 256
# number of input channels to each branch
multibranch_input_channels = 3
num_branches = 3
# None, or list of slices' indices ordering them before splitting between branches,
# length must be equal to num_branches * multibranch_input_channels
multibranch_channel_indices = None
# multibranch_channel_indices = [0, 1, 2, 1, 2, 3, 2, 3, 4]
contextual_attention = False
spatial_attention = False
class Config(BaseConfig):
train_out_dir = BaseConfig.model_outdir + '/seg0003_ours_any_iou'
train_dataset_file = '5fold.csv'
val_dataset_file = '5fold.csv'
test_dataset_file = 'test.csv'
data_version = '3d' # '3d', 'npy', 'npy256' etc.
val_folds = [0]
train_folds = get_train_folds(val_folds)
folds_str = '/fold' + get_val_folds_str(val_folds)
train_out_dir += folds_str
backbone = 'se_resnext50_32x4d'
# 'imagenet', None or path to weights
# pretrained = 'imagenet'
pretrained = BaseConfig.model_outdir + f'/0014_384/{folds_str}/models/*'
pretrained = sorted(glob.glob(pretrained))[-1]
lr = 2e-4
decoder_lr = 2e-4
encoder_lr = 8e-6
batch_size = 14
weight_decay = 0.001
optimizer = 'radam'
scheduler = {
'name': 'flat_anneal',
'flat_iterations': 3000,
'anneal_iterations': 7000,
'min_lr': 1e-6
}
gpus = [0]
num_workers = 3 * len(gpus)
max_epoch = 60
negative_data_steps = [2000, 4500, 7000]
# negative_data_steps = None
num_slices = 3 # must be odd
pre_crop_size = 400
train_image_size = 448
crop_size = 384
random_crop = False
center_crop = False
shift_value = 0.04
vertical_flip = False
pixel_augment = False
elastic_transform = False
use_cdf = True
augment = True
# used only if use_cdf is False
min_hu_value = 20
max_hu_value = 100
class Config(BaseConfig):
train_out_dir = BaseConfig.model_outdir + '/0038_7s_res50_400'
gpus = [0]
train_dataset_file = '5fold-rev3.csv'
val_dataset_file = '5fold.csv'
test_dataset_file = 'test.csv'
data_version = '3d' # '3d', 'npy', 'npy256' etc.
use_cq500 = False
val_folds = [0]
train_folds = get_train_folds(val_folds)
folds_str = '/fold' + get_val_folds_str(val_folds)
train_out_dir += folds_str
backbone = 'se_resnext50'
# 'imagenet', None or path to weights
pretrained = BaseConfig.model_outdir + f'/0014_384/{folds_str}/models/*'
pretrained = sorted(glob.glob(pretrained))[-1]
lr = 1e-4
batch_size = 24 # 16 (3, 512, 512) images fits on TITAN XP
accumulate_grad_batches = 1
dropout = 0
weight_decay = 0.001
optimizer = 'radam'
scheduler = {
'name': 'flat_anneal',
'flat_iterations': 16000,
'anneal_iterations': 32000,
'min_lr': 1e-7
}
freeze_backbone_iterations = 0
freeze_first_layer = False
num_workers = 3 * len(gpus)
max_epoch = 20
append_masks = False
num_slices = 7 # must be odd
pre_crop_size = 400
padded_size = None
crop_size = 400
shift_limit = 0.1
random_crop = False
vertical_flip = False
pixel_augment = False
elastic_transform = False
use_cdf = True
augment = True
# used only if use_cdf is False
min_hu_value = 20
max_hu_value = 100
balancing = False
# 'epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid', 'subdural', no_bleeding
probas = [0.1, 0.14, 0.14, 0.14, 0.14, 0.34]
multibranch = False
# use 3d conv to merge features from different branches
multibranch3d = False
multibranch_embedding = 256
# number of input channels to each branch
multibranch_input_channels = 3
num_branches = 3
# None, or list of slices' indices ordering them before splitting between branches,
# length must be equal to num_branches * multibranch_input_channels
multibranch_channel_indices = None
# multibranch_channel_indices = [0, 1, 2, 1, 2, 3, 2, 3, 4]
contextual_attention = False
spatial_attention = False