import math
from typing import Any, Dict, List, Optional, Tuple
import torch
import torchvision
from torch import nn, Tensor
from .image_list import ImageList
from .roi_heads import paste_masks_in_image
@torch.jit.unused
def _get_shape_onnx(image: Tensor) -> Tensor:
from torch.onnx import operators
return operators.shape_as_tensor(image)[-2:]
@torch.jit.unused
def _fake_cast_onnx(v: Tensor) -> float:
# ONNX requires a tensor but here we fake its type for JIT.
return v
def _resize_image_and_masks(
image: Tensor,
self_min_size: int,
self_max_size: int,
target: Optional[Dict[str, Tensor]] = None,
fixed_size: Optional[Tuple[int, int]] = None,
) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
if torchvision._is_tracing():
im_shape = _get_shape_onnx(image)
elif torch.jit.is_scripting():
im_shape = torch.tensor(image.shape[-2:])
else:
im_shape = image.shape[-2:]
size: Optional[List[int]] = None
scale_factor: Optional[float] = None
recompute_scale_factor: Optional[bool] = None
if fixed_size is not None:
size = [fixed_size[1], fixed_size[0]]
else:
if torch.jit.is_scripting() or torchvision._is_tracing():
min_size = torch.min(im_shape).to(dtype=torch.float32)
max_size = torch.max(im_shape).to(dtype=torch.float32)
self_min_size_f = float(self_min_size)
self_max_size_f = float(self_max_size)
scale = torch.min(self_min_size_f / min_size, self_max_size_f / max_size)
if torchvision._is_tracing():
scale_factor = _fake_cast_onnx(scale)
else:
scale_factor = scale.item()
else:
# Do it the normal way
min_size = min(im_shape)
max_size = max(im_shape)
scale_factor = min(self_min_size / min_size, self_max_size / max_size)
recompute_scale_factor = True
image = torch.nn.functional.interpolate(
image[None],
size=size,
scale_factor=scale_factor,
mode="bilinear",
recompute_scale_factor=recompute_scale_factor,
align_corners=False,
)[0]
if target is None:
return image, target
if "masks" in target:
mask = target["masks"]
mask = torch.nn.functional.interpolate(
mask[:, None].float(), size=size, scale_factor=scale_factor, recompute_scale_factor=recompute_scale_factor
)[:, 0].byte()
target["masks"] = mask
return image, target
class GeneralizedRCNNTransform(nn.Module):
"""
Performs input / target transformation before feeding the data to a GeneralizedRCNN
model.
The transformations it performs are:
- input normalization (mean subtraction and std division)
- input / target resizing to match min_size / max_size
It returns a ImageList for the inputs, and a List[Dict[Tensor]] for the targets
"""
def __init__(
self,
min_size: int,
max_size: int,
image_mean: List[float],
image_std: List[float],
size_divisible: int = 32,
fixed_size: Optional[Tuple[int, int]] = None,
**kwargs: Any,
):
super().__init__()
if not isinstance(min_size, (list, tuple)):
min_size = (min_size,)
self.min_size = min_size
self.max_size = max_size
self.image_mean = image_mean
self.image_std = image_std
self.size_divisible = size_divisible
self.fixed_size = fixed_size
self._skip_resize = kwargs.pop("_skip_resize", False)
def forward(
self, images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]] = None
) -> Tuple[ImageList, Optional[List[Dict[str, Tensor]]]]:
images = [img for img in images]
if targets is not None:
# make a copy of targets to avoid modifying it in-place
# once torchscript supports dict comprehension
# this can be simplified as follows
# targets = [{k: v for k,v in t.items()} for t in targets]
targets_copy: List[Dict[str, Tensor]] = []
for t in targets:
data: Dict[str, Tensor] = {}
for k, v in t.items():
data[k] = v
targets_copy.append(data)
targets = targets_copy
for i in range(len(images)):
image = images[i]
target_index = targets[i] if targets is not None else None
if image.dim() != 3:
raise ValueError(f"images is expected to be a list of 3d tensors of shape [C, H, W], got {image.shape}")
image = self.normalize(image)
image, target_index = self.resize(image, target_index)
images[i] = image
if targets is not None and target_index is not None:
targets[i] = target_index
image_sizes = [img.shape[-2:] for img in images]
images = self.batch_images(images, size_divisible=self.size_divisible)
image_sizes_list: List[Tuple[int, int]] = []
for image_size in image_sizes:
torch._assert(
len(image_size) == 2,
f"Input tensors expected to have in the last two elements H and W, instead got {image_size}",
)
image_sizes_list.append((image_size[0], image_size[1]))
image_list = ImageList(images, image_sizes_list)
return image_list, targets
def normalize(self, image: Tensor) -> Tensor:
if not image.is_floating_point():
raise TypeError(
f"Expected input images to be of floating type (in range [0, 1]), "
f"but found type {image.dtype} instead"
)
dtype, device = image.dtype, image.device
mean = torch.as_tensor(self.image_mean, dtype=dtype, device=device)
std = torch.as_tensor(self.image_std, dtype=dtype, device=device)
return (image - mean[:, None, None]) / std[:, None, None]
def torch_choice(self, k: List[int]) -> int:
"""
Implements `random.choice` via torch ops, so it can be compiled with
TorchScript and we use PyTorch's RNG (not native RNG)
"""
index = int(torch.empty(1).uniform_(0.0, float(len(k))).item())
return k[index]
def resize(
self,
image: Tensor,
target: Optional[Dict[str, Tensor]] = None,
) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
h, w = image.shape[-2:]
if self.training:
if self._skip_resize:
return image, target
size = self.torch_choice(self.min_size)
else:
size = self.min_size[-1]
image, target = _resize_image_and_masks(image, size, self.max_size, target, self.fixed_size)
if target is None:
return image, target
bbox = target["boxes"]
bbox = resize_boxes(bbox, (h, w), image.shape[-2:])
target["boxes"] = bbox
if "keypoints" in target:
keypoints = target["keypoints"]
keypoints = resize_keypoints(keypoints, (h, w), image.shape[-2:])
target["keypoints"] = keypoints
return image, target
# _onnx_batch_images() is an implementation of
# batch_images() that is supported by ONNX tracing.
@torch.jit.unused
def _onnx_batch_images(self, images: List[Tensor], size_divisible: int = 32) -> Tensor:
max_size = []
for i in range(images[0].dim()):
max_size_i = torch.max(torch.stack([img.shape[i] for img in images]).to(torch.float32)).to(torch.int64)
max_size.append(max_size_i)
stride = size_divisible
max_size[1] = (torch.ceil((max_size[1].to(torch.float32)) / stride) * stride).to(torch.int64)
max_size[2] = (torch.ceil((max_size[2].to(torch.float32)) / stride) * stride).to(torch.int64)
max_size = tuple(max_size)
# work around for
# pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
# which is not yet supported in onnx
padded_imgs = []
for img in images:
padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
padded_imgs.append(padded_img)
return torch.stack(padded_imgs)
def max_by_axis(self, the_list: List[List[int]]) -> List[int]:
maxes = the_list[0]
for sublist in the_list[1:]:
for index, item in enumerate(sublist):
maxes[index] = max(maxes[index], item)
return maxes
def batch_images(self, images: List[Tensor], size_divisible: int = 32) -> Tensor:
if torchvision._is_tracing():
# batch_images() does not export well to ONNX
# call _onnx_batch_images() instead
return self._onnx_batch_images(images, size_divisible)
max_size = self.max_by_axis([list(img.shape) for img in images])
stride = float(size_divisible)
max_size = list(max_size)
max_size[1] = int(math.ceil(float(max_size[1]) / stride) * stride)
max_size[2] = int(math.ceil(float(max_size[2]) / stride) * stride)
batch_shape = [len(images)] + max_size
batched_imgs = images[0].new_full(batch_shape, 0)
for i in range(batched_imgs.shape[0]):
img = images[i]
batched_imgs[i, : img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
return batched_imgs
def postprocess(
self,
result: List[Dict[str, Tensor]],
image_shapes: List[Tuple[int, int]],
original_image_sizes: List[Tuple[int, int]],
) -> List[Dict[str, Tensor]]:
if self.training:
return result
for i, (pred, im_s, o_im_s) in enumerate(zip(result, image_shapes, original_image_sizes)):
boxes = pred["boxes"]
boxes = resize_boxes(boxes, im_s, o_im_s)
result[i]["boxes"] = boxes
if "masks" in pred:
masks = pred["masks"]
masks = paste_masks_in_image(masks, boxes, o_im_s)
result[i]["masks"] = masks
if "keypoints" in pred:
keypoints = pred["keypoints"]
keypoints = resize_keypoints(keypoints, im_s, o_im_s)
result[i]["keypoints"] = keypoints
return result
def __repr__(self) -> str:
format_string = f"{self.__class__.__name__}("
_indent = "\n "
format_string += f"{_indent}Normalize(mean={self.image_mean}, std={self.image_std})"
format_string += f"{_indent}Resize(min_size={self.min_size}, max_size={self.max_size}, mode='bilinear')"
format_string += "\n)"
return format_string
def resize_keypoints(keypoints: Tensor, original_size: List[int], new_size: List[int]) -> Tensor:
ratios = [
torch.tensor(s, dtype=torch.float32, device=keypoints.device)
/ torch.tensor(s_orig, dtype=torch.float32, device=keypoints.device)
for s, s_orig in zip(new_size, original_size)
]
ratio_h, ratio_w = ratios
resized_data = keypoints.clone()
if torch._C._get_tracing_state():
resized_data_0 = resized_data[:, :, 0] * ratio_w
resized_data_1 = resized_data[:, :, 1] * ratio_h
resized_data = torch.stack((resized_data_0, resized_data_1, resized_data[:, :, 2]), dim=2)
else:
resized_data[..., 0] *= ratio_w
resized_data[..., 1] *= ratio_h
return resized_data
def resize_boxes(boxes: Tensor, original_size: List[int], new_size: List[int]) -> Tensor:
ratios = [
torch.tensor(s, dtype=torch.float32, device=boxes.device)
/ torch.tensor(s_orig, dtype=torch.float32, device=boxes.device)
for s, s_orig in zip(new_size, original_size)
]
ratio_height, ratio_width = ratios
xmin, ymin, xmax, ymax = boxes.unbind(1)
xmin = xmin * ratio_width
xmax = xmax * ratio_width
ymin = ymin * ratio_height
ymax = ymax * ratio_height
return torch.stack((xmin, ymin, xmax, ymax), dim=1)