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Unify onnx and JIT resize implementations #3654

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Merged
merged 3 commits into from
Apr 9, 2021
Merged

Unify onnx and JIT resize implementations #3654

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599a1a5
Make two methods as similar as possible.
datumbox Apr 9, 2021
b31127b
Introducing conditional fake casting.
datumbox Apr 9, 2021
0f95df5
Change the casting mechanism.
datumbox Apr 9, 2021
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50 changes: 23 additions & 27 deletions torchvision/models/detection/transform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,36 +10,35 @@


@torch.jit.unused
def _resize_image_and_masks_onnx(image, self_min_size, self_max_size, target):
# type: (Tensor, float, float, Optional[Dict[str, Tensor]]) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]
def _get_shape_onnx(image):
# type: (Tensor) -> Tensor
from torch.onnx import operators
im_shape = operators.shape_as_tensor(image)[-2:]
min_size = torch.min(im_shape).to(dtype=torch.float32)
max_size = torch.max(im_shape).to(dtype=torch.float32)
scale_factor = torch.min(self_min_size / min_size, self_max_size / max_size)

image = torch.nn.functional.interpolate(
image[None], scale_factor=scale_factor, mode='bilinear', recompute_scale_factor=True,
align_corners=False)[0]
return operators.shape_as_tensor(image)[-2:]

if target is None:
return image, target

if "masks" in target:
mask = target["masks"]
mask = F.interpolate(mask[:, None].float(), scale_factor=scale_factor, recompute_scale_factor=True)[:, 0].byte()
target["masks"] = mask
return image, target
@torch.jit.unused
def _fake_cast_onnx(v):
# type: (Tensor) -> float
# ONNX requires a tensor but here we fake its type for JIT.
return v


def _resize_image_and_masks(image, self_min_size, self_max_size, target):
# type: (Tensor, float, float, Optional[Dict[str, Tensor]]) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]
im_shape = torch.tensor(image.shape[-2:])
min_size = float(torch.min(im_shape))
max_size = float(torch.max(im_shape))
scale_factor = self_min_size / min_size
if max_size * scale_factor > self_max_size:
scale_factor = self_max_size / max_size
if torchvision._is_tracing():
im_shape = _get_shape_onnx(image)
else:
im_shape = torch.tensor(image.shape[-2:])

min_size = torch.min(im_shape).to(dtype=torch.float32)
max_size = torch.max(im_shape).to(dtype=torch.float32)
scale = torch.min(self_min_size / min_size, self_max_size / max_size)

if torchvision._is_tracing():
scale_factor = _fake_cast_onnx(scale)
else:
scale_factor = scale.item()

image = torch.nn.functional.interpolate(
image[None], scale_factor=scale_factor, mode='bilinear', recompute_scale_factor=True,
align_corners=False)[0]
Expand Down Expand Up @@ -145,10 +144,7 @@ def resize(self, image, target):
else:
# FIXME assume for now that testing uses the largest scale
size = float(self.min_size[-1])
if torchvision._is_tracing():
image, target = _resize_image_and_masks_onnx(image, size, float(self.max_size), target)
else:
image, target = _resize_image_and_masks(image, size, float(self.max_size), target)
image, target = _resize_image_and_masks(image, size, float(self.max_size), target)

if target is None:
return image, target
Expand Down