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[FIX] Resolve #19 by fixing broken import #20

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[FIX] Resolve #19 by fixing broken import #20

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29 changes: 16 additions & 13 deletions interpol/autograd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,10 +10,13 @@
grid_grad, grid_grad_backward)
from .utils import fake_decorator
try:
from torch.cuda.amp import custom_fwd, custom_bwd
from torch.amp import custom_fwd, custom_bwd
except (ModuleNotFoundError, ImportError):
custom_fwd = custom_bwd = fake_decorator

# Use cuda if available
device_type = 'cuda' if torch.cuda.is_available() else 'cpu'


def make_list(x):
if not isinstance(x, (list, tuple)):
Expand Down Expand Up @@ -125,7 +128,7 @@ def inter_to_nitorch(inter, as_type='str'):
class GridPull(torch.autograd.Function):

@staticmethod
@custom_fwd(cast_inputs=torch.float32)
@custom_fwd(cast_inputs=torch.float32, device_type=device_type)
def forward(ctx, input, grid, interpolation, bound, extrapolate):

bound = bound_to_nitorch(make_list(bound), as_type='int')
Expand All @@ -143,7 +146,7 @@ def forward(ctx, input, grid, interpolation, bound, extrapolate):
return output

@staticmethod
@custom_bwd
@custom_bwd(device_type=device_type)
def backward(ctx, grad):
var = ctx.saved_tensors
opt = ctx.opt
Expand All @@ -155,7 +158,7 @@ def backward(ctx, grad):
class GridPush(torch.autograd.Function):

@staticmethod
@custom_fwd(cast_inputs=torch.float32)
@custom_fwd(cast_inputs=torch.float32, device_type=device_type)
def forward(ctx, input, grid, shape, interpolation, bound, extrapolate):

bound = bound_to_nitorch(make_list(bound), as_type='int')
Expand All @@ -173,7 +176,7 @@ def forward(ctx, input, grid, shape, interpolation, bound, extrapolate):
return output

@staticmethod
@custom_bwd
@custom_bwd(device_type=device_type)
def backward(ctx, grad):
var = ctx.saved_tensors
opt = ctx.opt
Expand All @@ -185,7 +188,7 @@ def backward(ctx, grad):
class GridCount(torch.autograd.Function):

@staticmethod
@custom_fwd(cast_inputs=torch.float32)
@custom_fwd(cast_inputs=torch.float32, device_type=device_type)
def forward(ctx, grid, shape, interpolation, bound, extrapolate):

bound = bound_to_nitorch(make_list(bound), as_type='int')
Expand All @@ -203,7 +206,7 @@ def forward(ctx, grid, shape, interpolation, bound, extrapolate):
return output

@staticmethod
@custom_bwd
@custom_bwd(device_type=device_type)
def backward(ctx, grad):
var = ctx.saved_tensors
opt = ctx.opt
Expand All @@ -216,7 +219,7 @@ def backward(ctx, grad):
class GridGrad(torch.autograd.Function):

@staticmethod
@custom_fwd(cast_inputs=torch.float32)
@custom_fwd(cast_inputs=torch.float32, device_type=device_type)
def forward(ctx, input, grid, interpolation, bound, extrapolate):

bound = bound_to_nitorch(make_list(bound), as_type='int')
Expand All @@ -234,7 +237,7 @@ def forward(ctx, input, grid, interpolation, bound, extrapolate):
return output

@staticmethod
@custom_bwd
@custom_bwd(device_type=device_type)
def backward(ctx, grad):
var = ctx.saved_tensors
opt = ctx.opt
Expand All @@ -248,7 +251,7 @@ def backward(ctx, grad):
class SplineCoeff(torch.autograd.Function):

@staticmethod
@custom_fwd
@custom_fwd(device_type=device_type)
def forward(ctx, input, bound, interpolation, dim, inplace):

bound = bound_to_nitorch(make_list(bound)[0], as_type='int')
Expand All @@ -265,7 +268,7 @@ def forward(ctx, input, bound, interpolation, dim, inplace):
return output

@staticmethod
@custom_bwd
@custom_bwd(device_type=device_type)
def backward(ctx, grad):
# symmetric filter -> backward == forward
# (I don't know if I can write into grad, so inplace=False to be safe)
Expand All @@ -276,7 +279,7 @@ def backward(ctx, grad):
class SplineCoeffND(torch.autograd.Function):

@staticmethod
@custom_fwd
@custom_fwd(device_type=device_type)
def forward(ctx, input, bound, interpolation, dim, inplace):

bound = bound_to_nitorch(make_list(bound), as_type='int')
Expand All @@ -293,7 +296,7 @@ def forward(ctx, input, bound, interpolation, dim, inplace):
return output

@staticmethod
@custom_bwd
@custom_bwd(device_type=device_type)
def backward(ctx, grad):
# symmetric filter -> backward == forward
# (I don't know if I can write into grad, so inplace=False to be safe)
Expand Down