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6442 device specific heads for dynunet #6484

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6442 device specific heads for dynunet #6484

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15 changes: 9 additions & 6 deletions monai/networks/nets/dynunet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@

# isort: dont-add-import: from __future__ import annotations

from typing import List, Optional, Sequence, Tuple, Union
from typing import Dict, List, Optional, Sequence, Tuple, Union

import torch
import torch.nn as nn
Expand All @@ -32,7 +32,7 @@ class DynUNetSkipLayer(nn.Module):
forward passes of the network.
"""

heads: Optional[List[torch.Tensor]]
heads: Optional[Dict[str, List[torch.Tensor]]]

def __init__(self, index, downsample, upsample, next_layer, heads=None, super_head=None):
super().__init__()
Expand All @@ -48,7 +48,7 @@ def forward(self, x):
nextout = self.next_layer(downout)
upout = self.upsample(nextout, downout)
if self.super_head is not None and self.heads is not None and self.index > 0:
self.heads[self.index - 1] = self.super_head(upout)
self.heads[str(upout.device)][self.index - 1] = self.super_head(upout)

return upout

Expand Down Expand Up @@ -169,7 +169,7 @@ def __init__(
self.deep_supervision = deep_supervision
self.deep_supr_num = deep_supr_num
# initialize the typed list of supervision head outputs so that Torchscript can recognize what's going on
self.heads: List[torch.Tensor] = [torch.rand(1)] * self.deep_supr_num
self.heads: Dict[str, List[torch.Tensor]] = {"device": [torch.rand(1)] * self.deep_supr_num}
if self.deep_supervision:
self.deep_supervision_heads = self.get_deep_supervision_heads()
self.check_deep_supr_num()
Expand Down Expand Up @@ -266,12 +266,15 @@ def check_filters(self):
self.filters = filters[: len(self.strides)]

def forward(self, x):
if self.deep_supervision:
self.heads[str(x.device)] = list(self.heads["device"])
out = self.skip_layers(x)
out = self.output_block(out)
if self.training and self.deep_supervision:
out_all = torch.zeros(out.shape[0], len(self.heads) + 1, *out.shape[1:], device=out.device, dtype=out.dtype)
heads = self.heads[str(x.device)]
out_all = torch.zeros(out.shape[0], len(heads) + 1, *out.shape[1:], device=out.device, dtype=out.dtype)
out_all[:, 0] = out
for idx, feature_map in enumerate(self.heads):
for idx, feature_map in enumerate(heads):
out_all[:, idx + 1] = interpolate(feature_map, out.shape[2:])
return out_all
return out
Expand Down