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Reduce peak memory in mid_cnn fusion and load checkpoints to CPU first #2

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Merged
sezginerr merged 1 commit into from
Apr 17, 2026
+6 −4
Merged

Reduce peak memory in mid_cnn fusion and load checkpoints to CPU first #2

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10 changes: 6 additions & 4 deletions contrastive-pretraining/mr_rate/mr_rate/mr_rate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -275,9 +275,11 @@ def _encode_visual_tokens(self, image, real_volume_mask, vis_proj_layer,
merged = vis_proj_layer(enc)

elif self.fusion_mode == "mid_cnn":
flat_img = rearrange(image, 'b r c d h w -> (b r) c d h w')
cnn_features = self.run_checkpoint(self.visual_transformer.forward_cnn, flat_img)
cnn_features = rearrange(cnn_features, '(b r) t h w d -> b r t h w d', r=r)
cnn_list = []
for i in range(r):
feat_i = self.run_checkpoint(self.visual_transformer.forward_cnn, image[:, i])
cnn_list.append(feat_i)
cnn_features = torch.stack(cnn_list, dim=1)
m = real_volume_mask.view(b, r, 1, 1, 1, 1).to(cnn_features.dtype)
merged = (cnn_features * m).sum(1) / m.sum(1).clamp(min=1.0)
enc = self.run_checkpoint(self.visual_transformer.forward_transformer, merged)
Expand Down Expand Up @@ -347,7 +349,7 @@ def load_state_dict(self, *args, **kwargs):
def load(self, path):
path = Path(path)
assert path.exists()
pt = torch.load(str(path))
pt = torch.load(str(path), map_location="cpu")
clean_state = {}
for k, v in pt.items():
if k.startswith("module."):
Expand Down
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