def __init__(self, structure, n_chans_in, n_chans_out):
super().__init__()
first_channels = structure[0][0][0]
last_channels = structure[0][-1][-1]
self.unet = nn.Sequential(
nn.Conv2d(n_chans_in,
first_channels,
kernel_size=3,
padding=1,
bias=False),
layers.FPN(layers.ResBlock2d,
nn.MaxPool2d(kernel_size=2, ceil_mode=True),
nn.Identity(),
lambda left, down: torch.add(
*layers.interpolate_to_left(left, down, 1)),
structure,
kernel_size=3,
padding=1))
self.out = nn.Sequential(
layers.ResBlock2d(last_channels,
last_channels,
kernel_size=3,
padding=1),
layers.PreActivation2d(last_channels, n_chans_out, kernel_size=1))
train_ids, val_ids, test_ids = split[int(FOLD)]
# batch iterator
batch_iter = Infinite(
load_by_random_id(train_dataset.load_image, train_dataset.load_gt, ids=train_ids),
unpack_args(tumor_sampling, patch_size=PATCH_SIZE, tumor_p=.5),
random_apply(.5, unpack_args(lambda image, gt: (np.flip(image, 1), np.flip(gt, 0)))),
batch_size=CONFIG['batch_size'], batches_per_epoch=CONFIG['batches_per_epoch']
)
# model
model = nn.Sequential(
nn.Conv3d(dataset.n_modalities, 8, kernel_size=3, padding=1),
layers.FPN(
layers.ResBlock3d, downsample=nn.MaxPool3d(2, ceil_mode=True), upsample=nn.Identity,
merge=lambda left, down: torch.cat(layers.interpolate_to_left(left, down, 'trilinear'), dim=1),
structure=[
[[8, 8, 8], [16, 8, 8]],
[[8, 16, 16], [32, 16, 8]],
[[16, 32, 32], [64, 32, 16]],
[[32, 64, 64], [128, 64, 32]],
[[64, 128, 128], [256, 128, 64]],
[[128, 256, 256], [512, 256, 128]],
[256, 512, 256]
],
kernel_size=3, padding=1
),
layers.PreActivation3d(8, dataset.n_classes, kernel_size=1)
).to(CONFIG['device'])
criterion = nn.CrossEntropyLoss()
random_apply(
.5,
unpack_args(lambda image, gt: (np.flip(image, 1), np.flip(gt, 0)))),
apply_at(1, prepend_dims),
apply_at(1, np.float32),
batch_size=CONFIG['batch_size'],
batches_per_epoch=CONFIG['batches_per_epoch'])
# model
model = nn.Sequential(
nn.Conv3d(dataset.n_modalities, 8, kernel_size=3, padding=1),
layers.FPN(layers.ResBlock3d,
downsample=nn.MaxPool3d(2, ceil_mode=True),
upsample=nn.Identity,
merge=lambda left, down: torch.cat(
layers.interpolate_to_left(left, down, 'trilinear'), dim=1),
structure=[[[8, 8, 8], [16, 8, 8]], [[8, 16, 16], [32, 16, 8]],
[[16, 32, 32], [64, 32, 16]],
[[32, 64, 64], [128, 64, 32]],
[[64, 128, 128], [256, 128, 64]],
[[128, 256, 256], [512, 256, 128]], [256, 512, 256]],
kernel_size=3,
padding=1),
layers.PreActivation3d(8, 1, kernel_size=1)).to(CONFIG['device'])
criterion = nn.BCEWithLogitsLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=CONFIG['lr'])
# predict
@patches_grid(patch_size=PATCH_SIZE, stride=7 * PATCH_SIZE // 8)