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Fix Dtypes for torch and numpy #65

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Merged
michaelmckinsey1 merged 1 commit into from
May 11, 2026
Merged

Fix Dtypes for torch and numpy #65

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5 changes: 4 additions & 1 deletion ScaFFold/utils/data_types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,10 @@
# Masks are values 0 <= x <= n_categories
MASK_DTYPE = np.uint16
# Volumes/img are 0 <= x <= 1
VOLUME_DTYPE = np.float32
VOLUME_DTYPE_NAME = "float32"
VOLUME_NP_DTYPE = getattr(np, VOLUME_DTYPE_NAME)
VOLUME_TORCH_DTYPE = getattr(torch, VOLUME_DTYPE_NAME)
VOLUME_DTYPE = VOLUME_NP_DTYPE

# Shared AMP dtype selection for torch.autocast.
AMP_DTYPE = torch.bfloat16
10 changes: 6 additions & 4 deletions ScaFFold/utils/trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@

from ScaFFold.utils.checkpointing import CheckpointManager
from ScaFFold.utils.data_loading import FractalDataset, SpatialShardSpec
from ScaFFold.utils.data_types import AMP_DTYPE, VOLUME_DTYPE
from ScaFFold.utils.data_types import AMP_DTYPE, VOLUME_TORCH_DTYPE
from ScaFFold.utils.dice_score import compute_sharded_dice
from ScaFFold.utils.distributed import get_local_rank, get_world_rank, get_world_size

Expand Down Expand Up @@ -436,7 +436,7 @@ def warmup(self):

images = images.to(
device=self.device,
dtype=VOLUME_DTYPE,
dtype=VOLUME_TORCH_DTYPE,
memory_format=torch.channels_last_3d,
non_blocking=True,
)
Expand Down Expand Up @@ -611,7 +611,7 @@ def train(self):
begin_code_region("image_to_device")
images = images.to(
device=self.device,
dtype=VOLUME_DTYPE,
dtype=VOLUME_TORCH_DTYPE,
memory_format=torch.channels_last_3d, # NDHWC (channels last) vs NCDHW (channels first)
non_blocking=True,
)
Expand Down Expand Up @@ -749,7 +749,9 @@ def train(self):
self.config.n_categories,
self.config._parallel_strategy,
)
dice_info = torch.tensor([dice_sum, numsamples], dtype=VOLUME_DTYPE)
dice_info = torch.tensor(
[dice_sum, numsamples], dtype=VOLUME_TORCH_DTYPE
)
if self.config.dist:
dice_info = dice_info.to(device=self.device)
torch.distributed.all_reduce(
Expand Down
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