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Fix inference with cpu only configs #9

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Merged
wyli merged 1 commit into from
Jul 20, 2023
Merged

Fix inference with cpu only configs #9

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2 changes: 1 addition & 1 deletion monailabel/monaivista/lib/infers/vista_point_2pt5.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@ def pre_transforms(self, data=None) -> Sequence[Callable]:
]

def inferer(self, data=None) -> Inferer:
return VISTASliceInferer()
return VISTASliceInferer(device=data.get("device") if data else None)

def inverse_transforms(self, data=None):
return []
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23 changes: 17 additions & 6 deletions monailabel/monaivista/lib/model/vista_point_2pt5/inferer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,14 +238,21 @@ def update_slice(
continue

inputs = inputs_l[..., start_idx - (n_z_slices // 2) : start_idx + (n_z_slices // 2) + 1].permute(2, 0, 1)
if device and (device == "cuda" or isinstance(device, torch.device) and device.type == "cuda"):
inputs = inputs.cuda()
data, unique_labels = prepare_sam_val_input(
inputs.cuda(), class_prompts, point_prompts, start_idx, original_affine
inputs, class_prompts, point_prompts, start_idx, original_affine, device=device
)

predictor.eval()
with torch.cuda.amp.autocast():
outputs = predictor(data)
logit = outputs[0]["high_res_logits"]
if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
with torch.cuda.amp.autocast():
outputs = predictor(data)
logit = outputs[0]["high_res_logits"]
else:
with torch.cpu.amp.autocast():
outputs = predictor(data)
logit = outputs[0]["high_res_logits"]

out_list = torch.unbind(logit, dim=0)
y_pred = torch.stack(post_pred_slice(out_list)).float()
Expand Down Expand Up @@ -290,11 +297,15 @@ def iterate_all(
)
for start_idx in start_range:
inputs = inputs_l[..., start_idx - n_z_slices // 2 : start_idx + n_z_slices // 2 + 1].permute(2, 0, 1)
data, unique_labels = prepare_sam_val_input(inputs.cuda(), class_prompts, point_prompts, start_idx)
if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
inputs = inputs.cuda()
data, unique_labels = prepare_sam_val_input(inputs, class_prompts, point_prompts, start_idx, device=device)
predictor = predictor.eval()
with autocast():
if cachedEmbedding:
curr_embedding = cachedEmbedding[start_idx].cuda()
curr_embedding = cachedEmbedding[start_idx]
if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
curr_embedding = curr_embedding.cuda()
outputs = predictor.get_mask_prediction(data, curr_embedding)
else:
outputs = predictor(data)
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13 changes: 9 additions & 4 deletions monailabel/monaivista/lib/model/vista_point_2pt5/utils/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,14 +80,16 @@ def distributed_all_gather(
return tensor_list_out


def prepare_sam_val_input(inputs, class_prompts, point_prompts, start_idx, original_affine=None):
def prepare_sam_val_input(inputs, class_prompts, point_prompts, start_idx, original_affine=None, device=None):
# Don't exclude background in val but will ignore it in metric calculation
H, W = inputs.shape[1:]
foreground_all = point_prompts["foreground"]
background_all = point_prompts["background"]

class_list = [[i + 1] for i in class_prompts]
unique_labels = torch.tensor(class_list).long().cuda()
unique_labels = torch.tensor(class_list).long()
if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
unique_labels = unique_labels.cuda()

volume_point_coords = [cp for cp in foreground_all]
volume_point_labels = [1] * len(foreground_all)
Expand Down Expand Up @@ -129,8 +131,11 @@ def prepare_sam_val_input(inputs, class_prompts, point_prompts, start_idx, origi
prepared_input[0].update({"labels": unique_labels})

if point_coords:
point_coords = torch.tensor(point_coords).long().cuda()
point_labels = torch.tensor(point_labels).long().cuda()
point_coords = torch.tensor(point_coords).long()
point_labels = torch.tensor(point_labels).long()
if device == "cuda" or (isinstance(device, torch.device) and device.type == "cuda"):
point_coords = point_coords.cuda()
point_labels = point_labels.cuda()

prepared_input[0].update({"point_coords": point_coords, "point_labels": point_labels})

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