Python CropOrPadAndResimulate 예제들

예제 #1

파일: script_grid_search_l1_fourier.py 프로젝트: jmaces/robust-nets

# ----- load test data --------
test_data_params = {
    "mask_func":
    mask_func,
    "seed":
    1,
    "filter": [filter_acquisition_no_fs],
    "num_sym_slices":
    0,
    "multi_slice_gt":
    False,
    "keep_mask_as_func":
    True,
    "transform":
    torchvision.transforms.Compose([
        CropOrPadAndResimulate(n),
        Flatten(0, -3),
        Normalize(reduction="mean", use_target=True),
    ], ),
}
test_data = AlmostFixedMaskDataset
test_data = test_data("val", **test_data_params)

vols = range(30)
slices_in_vols = [test_data.get_slices_in_volume(vol_idx) for vol_idx in vols]
slices_selected = [
    range((lo + hi) // 2, (lo + hi) // 2 + 1) for lo, hi in slices_in_vols
]
samples = np.concatenate(slices_selected)

X_0 = torch.stack([test_data[s][2] for s in samples])

예제 #2

 torch.optim.Adam,
 "optimizer_params": [{
     "lr": 5e-5,
     "eps": 1e-4,
     "weight_decay": 1e-6
 }],
 "scheduler":
 torch.optim.lr_scheduler.StepLR,
 "scheduler_params": {
     "step_size": 1,
     "gamma": 1.0
 },
 "acc_steps": [1],
 "train_transform":
 torchvision.transforms.Compose([
     CropOrPadAndResimulate((320, 320)),
     Flatten(0, -3),
     Normalize(reduction="mean", use_target=True),
     Jitter(1.5e2, 0.0, 1.0),
 ]),
 "val_transform":
 torchvision.transforms.Compose([
     CropOrPadAndResimulate((320, 320)),
     Flatten(0, -3),
     Normalize(reduction="mean", use_target=True),
 ], ),
 "train_loader_params": {
     "shuffle": True,
     "num_workers": 8
 },
 "val_loader_params": {

예제 #3

파일: script_robustness_fourier_table_adv.py 프로젝트: jmaces/robust-nets

# ----- load test data --------
test_data_params = {
    "mask_func":
    cfg_rob.mask_func,
    "seed":
    1,
    "filter": [filter_acquisition_no_fs],
    "num_sym_slices":
    0,
    "multi_slice_gt":
    False,
    "keep_mask_as_func":
    True,
    "transform":
    torchvision.transforms.Compose([
        CropOrPadAndResimulate(cfg_rob.n),
        Flatten(0, -3),
        Normalize(reduction="mean", use_target=True),
    ], ),
}
test_data = AlmostFixedMaskDataset
test_data = test_data("val", **test_data_params)

vols = range(30)
slices_in_vols = [test_data.get_slices_in_volume(vol_idx) for vol_idx in vols]
slices_selected = [
    range((lo + hi) // 2, (lo + hi) // 2 + 1) for lo, hi in slices_in_vols
]
samples = np.concatenate(slices_selected)

X_0 = torch.stack([test_data[s][2] for s in samples])

예제 #4

 torch.optim.Adam,
 "optimizer_params": [{
     "lr": 1e-4,
     "eps": 1e-4,
     "weight_decay": 1e-5
 }],
 "scheduler":
 torch.optim.lr_scheduler.StepLR,
 "scheduler_params": {
     "step_size": 1,
     "gamma": 1.0
 },
 "acc_steps": [1],
 "train_transform":
 torchvision.transforms.Compose([
     CropOrPadAndResimulate((368, 368)),
     Flatten(0, -3),
     Normalize(reduction="mean", use_target=False),
     Jitter(1e1, 0.0, 1.0),
 ]),
 "val_transform":
 torchvision.transforms.Compose([
     CropOrPadAndResimulate((368, 368)),
     Flatten(0, -3),
     Normalize(reduction="mean", use_target=False),
 ], ),
 "train_loader_params": {
     "shuffle": True,
     "num_workers": 8
 },
 "val_loader_params": {