File size: 5,512 Bytes
b213d84 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
# Copyright (c) Facebook, Inc. and its affiliates.
from __future__ import division
from typing import Any, Dict, List, Optional, Tuple
import torch
from torch import device
from torch.nn import functional as F
from detectron2.layers.wrappers import move_device_like, shapes_to_tensor
class ImageList:
"""
Structure that holds a list of images (of possibly
varying sizes) as a single tensor.
This works by padding the images to the same size.
The original sizes of each image is stored in `image_sizes`.
Attributes:
image_sizes (list[tuple[int, int]]): each tuple is (h, w).
During tracing, it becomes list[Tensor] instead.
"""
def __init__(self, tensor: torch.Tensor, image_sizes: List[Tuple[int, int]]):
"""
Arguments:
tensor (Tensor): of shape (N, H, W) or (N, C_1, ..., C_K, H, W) where K >= 1
image_sizes (list[tuple[int, int]]): Each tuple is (h, w). It can
be smaller than (H, W) due to padding.
"""
self.tensor = tensor
self.image_sizes = image_sizes
def __len__(self) -> int:
return len(self.image_sizes)
def __getitem__(self, idx) -> torch.Tensor:
"""
Access the individual image in its original size.
Args:
idx: int or slice
Returns:
Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1
"""
size = self.image_sizes[idx]
return self.tensor[idx, ..., : size[0], : size[1]]
@torch.jit.unused
def to(self, *args: Any, **kwargs: Any) -> "ImageList":
cast_tensor = self.tensor.to(*args, **kwargs)
return ImageList(cast_tensor, self.image_sizes)
@property
def device(self) -> device:
return self.tensor.device
@staticmethod
def from_tensors(
tensors: List[torch.Tensor],
size_divisibility: int = 0,
pad_value: float = 0.0,
padding_constraints: Optional[Dict[str, int]] = None,
) -> "ImageList":
"""
Args:
tensors: a tuple or list of `torch.Tensor`, each of shape (Hi, Wi) or
(C_1, ..., C_K, Hi, Wi) where K >= 1. The Tensors will be padded
to the same shape with `pad_value`.
size_divisibility (int): If `size_divisibility > 0`, add padding to ensure
the common height and width is divisible by `size_divisibility`.
This depends on the model and many models need a divisibility of 32.
pad_value (float): value to pad.
padding_constraints (optional[Dict]): If given, it would follow the format as
{"size_divisibility": int, "square_size": int}, where `size_divisibility` will
overwrite the above one if presented and `square_size` indicates the
square padding size if `square_size` > 0.
Returns:
an `ImageList`.
"""
assert len(tensors) > 0
assert isinstance(tensors, (tuple, list))
for t in tensors:
assert isinstance(t, torch.Tensor), type(t)
assert t.shape[:-2] == tensors[0].shape[:-2], t.shape
image_sizes = [(im.shape[-2], im.shape[-1]) for im in tensors]
image_sizes_tensor = [shapes_to_tensor(x) for x in image_sizes]
max_size = torch.stack(image_sizes_tensor).max(0).values
if padding_constraints is not None:
square_size = padding_constraints.get("square_size", 0)
if square_size > 0:
# pad to square.
max_size[0] = max_size[1] = square_size
if "size_divisibility" in padding_constraints:
size_divisibility = padding_constraints["size_divisibility"]
if size_divisibility > 1:
stride = size_divisibility
# the last two dims are H,W, both subject to divisibility requirement
max_size = (max_size + (stride - 1)).div(stride, rounding_mode="floor") * stride
# handle weirdness of scripting and tracing ...
if torch.jit.is_scripting():
max_size: List[int] = max_size.to(dtype=torch.long).tolist()
else:
if torch.jit.is_tracing():
image_sizes = image_sizes_tensor
if len(tensors) == 1:
# This seems slightly (2%) faster.
# TODO: check whether it's faster for multiple images as well
image_size = image_sizes[0]
padding_size = [0, max_size[-1] - image_size[1], 0, max_size[-2] - image_size[0]]
batched_imgs = F.pad(tensors[0], padding_size, value=pad_value).unsqueeze_(0)
else:
# max_size can be a tensor in tracing mode, therefore convert to list
batch_shape = [len(tensors)] + list(tensors[0].shape[:-2]) + list(max_size)
device = (
None if torch.jit.is_scripting() else ("cpu" if torch.jit.is_tracing() else None)
)
batched_imgs = tensors[0].new_full(batch_shape, pad_value, device=device)
batched_imgs = move_device_like(batched_imgs, tensors[0])
for i, img in enumerate(tensors):
# Use `batched_imgs` directly instead of `img, pad_img = zip(tensors, batched_imgs)`
# Tracing mode cannot capture `copy_()` of temporary locals
batched_imgs[i, ..., : img.shape[-2], : img.shape[-1]].copy_(img)
return ImageList(batched_imgs.contiguous(), image_sizes)
|