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This commit is contained in:
Ishan S. Patel
2021-07-01 20:26:24 -04:00
parent 8b02bf9a8c
commit f46d193826
16 changed files with 433 additions and 146 deletions
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110
transforms.py
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@@ -28,8 +28,9 @@ class Compose(object):
class RandomHorizontalFlip(T.RandomHorizontalFlip):
def forward(self, image: Tensor,
target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
def forward(
self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
if torch.rand(1) < self.p:
image = F.hflip(image)
if target is not None:
@@ -45,15 +46,23 @@ class RandomHorizontalFlip(T.RandomHorizontalFlip):
class ToTensor(nn.Module):
def forward(self, image: Tensor,
target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
def forward(
self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
image = F.to_tensor(image)
return image, target
class RandomIoUCrop(nn.Module):
def __init__(self, min_scale: float = 0.3, max_scale: float = 1.0, min_aspect_ratio: float = 0.5,
max_aspect_ratio: float = 2.0, sampler_options: Optional[List[float]] = None, trials: int = 40):
def __init__(
self,
min_scale: float = 0.3,
max_scale: float = 1.0,
min_aspect_ratio: float = 0.5,
max_aspect_ratio: float = 2.0,
sampler_options: Optional[List[float]] = None,
trials: int = 40,
):
super().__init__()
# Configuration similar to https://github.com/weiliu89/caffe/blob/ssd/examples/ssd/ssd_coco.py#L89-L174
self.min_scale = min_scale
@@ -65,14 +74,19 @@ class RandomIoUCrop(nn.Module):
self.options = sampler_options
self.trials = trials
def forward(self, image: Tensor,
target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
def forward(
self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
if target is None:
raise ValueError("The targets can't be None for this transform.")
if isinstance(image, torch.Tensor):
if image.ndimension() not in {2, 3}:
raise ValueError('image should be 2/3 dimensional. Got {} dimensions.'.format(image.ndimension()))
raise ValueError(
"image should be 2/3 dimensional. Got {} dimensions.".format(
image.ndimension()
)
)
elif image.ndimension() == 2:
image = image.unsqueeze(0)
@@ -82,7 +96,9 @@ class RandomIoUCrop(nn.Module):
# sample an option
idx = int(torch.randint(low=0, high=len(self.options), size=(1,)))
min_jaccard_overlap = self.options[idx]
if min_jaccard_overlap >= 1.0: # a value larger than 1 encodes the leave as-is option
if (
min_jaccard_overlap >= 1.0
): # a value larger than 1 encodes the leave as-is option
return image, target
for _ in range(self.trials):
@@ -106,14 +122,22 @@ class RandomIoUCrop(nn.Module):
# check for any valid boxes with centers within the crop area
cx = 0.5 * (target["boxes"][:, 0] + target["boxes"][:, 2])
cy = 0.5 * (target["boxes"][:, 1] + target["boxes"][:, 3])
is_within_crop_area = (left < cx) & (cx < right) & (top < cy) & (cy < bottom)
is_within_crop_area = (
(left < cx) & (cx < right) & (top < cy) & (cy < bottom)
)
if not is_within_crop_area.any():
continue
# check at least 1 box with jaccard limitations
boxes = target["boxes"][is_within_crop_area]
ious = torchvision.ops.boxes.box_iou(boxes, torch.tensor([[left, top, right, bottom]],
dtype=boxes.dtype, device=boxes.device))
ious = torchvision.ops.boxes.box_iou(
boxes,
torch.tensor(
[[left, top, right, bottom]],
dtype=boxes.dtype,
device=boxes.device,
),
)
if ious.max() < min_jaccard_overlap:
continue
@@ -130,14 +154,21 @@ class RandomIoUCrop(nn.Module):
class RandomZoomOut(nn.Module):
def __init__(self, fill: Optional[List[float]] = None, side_range: Tuple[float, float] = (1., 4.), p: float = 0.5):
def __init__(
self,
fill: Optional[List[float]] = None,
side_range: Tuple[float, float] = (1.0, 4.0),
p: float = 0.5,
):
super().__init__()
if fill is None:
fill = [0., 0., 0.]
fill = [0.0, 0.0, 0.0]
self.fill = fill
self.side_range = side_range
if side_range[0] < 1. or side_range[0] > side_range[1]:
raise ValueError("Invalid canvas side range provided {}.".format(side_range))
if side_range[0] < 1.0 or side_range[0] > side_range[1]:
raise ValueError(
"Invalid canvas side range provided {}.".format(side_range)
)
self.p = p
@torch.jit.unused
@@ -146,11 +177,16 @@ class RandomZoomOut(nn.Module):
# We fake the type to make it work on JIT
return tuple(int(x) for x in self.fill) if is_pil else 0
def forward(self, image: Tensor,
target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
def forward(
self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
if isinstance(image, torch.Tensor):
if image.ndimension() not in {2, 3}:
raise ValueError('image should be 2/3 dimensional. Got {} dimensions.'.format(image.ndimension()))
raise ValueError(
"image should be 2/3 dimensional. Got {} dimensions.".format(
image.ndimension()
)
)
elif image.ndimension() == 2:
image = image.unsqueeze(0)
@@ -159,7 +195,9 @@ class RandomZoomOut(nn.Module):
orig_w, orig_h = F._get_image_size(image)
r = self.side_range[0] + torch.rand(1) * (self.side_range[1] - self.side_range[0])
r = self.side_range[0] + torch.rand(1) * (
self.side_range[1] - self.side_range[0]
)
canvas_width = int(orig_w * r)
canvas_height = int(orig_h * r)
@@ -176,9 +214,12 @@ class RandomZoomOut(nn.Module):
image = F.pad(image, [left, top, right, bottom], fill=fill)
if isinstance(image, torch.Tensor):
v = torch.tensor(self.fill, device=image.device, dtype=image.dtype).view(-1, 1, 1)
image[..., :top, :] = image[..., :, :left] = image[..., (top + orig_h):, :] = \
image[..., :, (left + orig_w):] = v
v = torch.tensor(self.fill, device=image.device, dtype=image.dtype).view(
-1, 1, 1
)
image[..., :top, :] = image[..., :, :left] = image[
..., (top + orig_h) :, :
] = image[..., :, (left + orig_w) :] = v
if target is not None:
target["boxes"][:, 0::2] += left
@@ -188,8 +229,14 @@ class RandomZoomOut(nn.Module):
class RandomPhotometricDistort(nn.Module):
def __init__(self, contrast: Tuple[float] = (0.5, 1.5), saturation: Tuple[float] = (0.5, 1.5),
hue: Tuple[float] = (-0.05, 0.05), brightness: Tuple[float] = (0.875, 1.125), p: float = 0.5):
def __init__(
self,
contrast: Tuple[float] = (0.5, 1.5),
saturation: Tuple[float] = (0.5, 1.5),
hue: Tuple[float] = (-0.05, 0.05),
brightness: Tuple[float] = (0.875, 1.125),
p: float = 0.5,
):
super().__init__()
self._brightness = T.ColorJitter(brightness=brightness)
self._contrast = T.ColorJitter(contrast=contrast)
@@ -197,11 +244,16 @@ class RandomPhotometricDistort(nn.Module):
self._saturation = T.ColorJitter(saturation=saturation)
self.p = p
def forward(self, image: Tensor,
target: Optional[Dict[str, Tensor]] = None) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
def forward(
self, image: Tensor, target: Optional[Dict[str, Tensor]] = None
) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
if isinstance(image, torch.Tensor):
if image.ndimension() not in {2, 3}:
raise ValueError('image should be 2/3 dimensional. Got {} dimensions.'.format(image.ndimension()))
raise ValueError(
"image should be 2/3 dimensional. Got {} dimensions.".format(
image.ndimension()
)
)
elif image.ndimension() == 2:
image = image.unsqueeze(0)