SNRAware / small /snraware_small_model.yaml
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backbone:
num_of_channels: 64
block_str:
- T1L1G1
- T1L1G1
block:
cell:
attention_type: conv
mixer_type: conv
window_size:
- 8
- 8
- 16
patch_size:
- 4
- 4
- 2
window_sizing_method: mixed
n_head: 64
scale_ratio_in_mixer: 4.0
normalize_Q_K: true
cosine_att: true
att_with_relative_position_bias: true
att_dropout_p: 0.0
dropout_p: 0.1
att_with_output_proj: true
norm_mode: layer
activation_func: prelu
upsample_method: linear
with_timer: false
temporal:
_target_: ifm.model.config.TemporalAttentionConfig
stride_qk:
- 1
- 1
- 1
spatial_local:
_target_: ifm.model.config.SpatialLocalConfig
spatial_global:
_target_: ifm.model.config.SpatialGlobalConfig
shuffle_in_window: false
convoluation:
_target_: ifm.model.config.ConvolutionConfig
conv_type: conv3d
spatial_local_3d:
_target_: ifm.model.config.SpatialLocal3DConfig
spatial_global_3d:
_target_: ifm.model.config.SpatialGlobal3DConfig
shuffle_in_window: false
spatial_vit:
_target_: ifm.model.config.SpatialViTConfig
vit_3d:
_target_: ifm.model.config.ViT3DConfig
swin_3d:
_target_: ifm.model.config.Swin3DConfig
_target_: ifm.model.config.BlockConfig
cell_type: sequential
block_dense_connection: false
_target_: ifm.model.config.HRNetConfig
name: HRnet
num_resolution_levels: 2
optim:
lr: 1.0e-05
weight_decay: 0.0
name: sophia
beta1: 0.9
beta2: 0.999
eps: 1.0e-08
rho: 0.01
scheduler:
name: OneCycleLR
pct_start: 0.3
anneal_strategy: cos
div_factor: 25
dataset:
_target_: ifm.mri.denoising.data.MRIDenoisingDataset
cutout_shape:
- 64
- 64
- 16
repetition: 1
min_noise_level: 0.1
max_noise_level: 128.0
kspace_filter_sigma:
- 0.8
- 1.0
- 1.5
- 2.0
- 2.25
kspace_filter_sigma_T:
- 0.25
- 0.5
- 0.65
- 0.85
- 1.0
- 1.5
- 2.0
- 2.25
prob_apply_filter_T: 0.2
pf_filter_ratio:
- 1.0
- 0.875
- 0.75
- 0.625
phase_resolution_ratio:
- 1.0
- 0.75
- 0.65
- 0.55
readout_resolution_ratio:
- 1.0
- 0.75
- 0.65
- 0.55
only_white_noise: false
ignore_gmap: false
add_noise: true
add_salt_pepper: true
salt_pepper_amount: 0.4
salt_pepper_prob: 0.4
add_poisson: true
poisson_prob: 0.4
shuffle_along_3rd_dim: true
shuffle_along_3rd_dim_prob: 0.1
matrix_size_adjust_ratio:
- 0.5
- 0.75
- 1.0
- 1.25
- 1.5
matrix_size_adjust_prob: 0.5
resolution_reduction_prob: 0.0
single_frame_mode: true
single_frame_mode_prob: 0.1
dicom_mode: false
trainer:
_target_: lightning.Trainer
max_epochs: 320
precision: 32
devices: 8
accelerator: gpu
strategy: ddp
num_nodes: 1
gradient_clip_val: 1.0
accumulate_grad_batches: 1
log_every_n_steps: 200
enable_progress_bar: true
enable_model_summary: true
enable_checkpointing: true
check_val_every_n_epoch: 16
val_check_interval: 1.0
fast_dev_run: false
overfit_batches: 0.0
limit_train_batches: 1.0
limit_val_batches: 1.0
limit_test_batches: 1.0
deterministic: false
benchmark: true
profiler: null
logging:
project: ifm-mri-denoising
run_name: ifm-mri-denoising-small-no-epoch
output_dir: /data1/inference_time/models/small
wandb_entity: biomed-signal-processing
wandb_dir: /data1/inference_time/models/small/wandb
use_wandb: true
save_ckpt_every_n_epochs: 16
save_batches_to_output_dir: false
log_train_batches: 16
log_val_batches: 16
log_test_batches: 32
tyger:
mode: false
dop: 8
step_mode:
total_steps: 0
steps_to_report: 200
steps_to_timing: 20000
steps_to_validate: 5000
steps_to_save_ckpts: 5000
train_data_dir: /data/imaging-fm-projects/mri/denoising/data/whole/tra
test_data_dir: /data/imaging-fm-projects/mri/denoising/data/whole/test
seed: null
max_epochs: 320
batch_size: 4
num_workers: 4
prefetch_factor: 8
loss:
- mse
- perpendicular
- perceptual
- charbonnier
- gaussian3d
loss_weights:
- 1.0
- 1.0
- 1.0
- 1.0
- 1.0
val_data_portion: 0.05
overlap_for_inference:
- 16
- 16
- 8
device_mesh:
- 8
- 2