7b89b8fc3f
Differential Revision: D90237984 fbshipit-source-id: 526fd760f303bf31be4f743bdcd77760496de0de
458 lines
15 KiB
Python
458 lines
15 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
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# pyre-unsafe
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"""
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Transforms and data augmentation for both image + bbox.
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"""
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import math
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import random
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from typing import Iterable
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import PIL
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import torch
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import torchvision.transforms as T
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import torchvision.transforms.functional as F
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from sam3.model.box_ops import box_xyxy_to_cxcywh
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from sam3.model.data_misc import interpolate
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def crop(image, target, region):
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cropped_image = F.crop(image, *region)
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target = target.copy()
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i, j, h, w = region
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# should we do something wrt the original size?
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target["size"] = torch.tensor([h, w])
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fields = ["labels", "area", "iscrowd", "positive_map"]
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if "boxes" in target:
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boxes = target["boxes"]
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max_size = torch.as_tensor([w, h], dtype=torch.float32)
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cropped_boxes = boxes - torch.as_tensor([j, i, j, i], dtype=torch.float32)
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cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
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cropped_boxes = cropped_boxes.clamp(min=0)
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area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
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target["boxes"] = cropped_boxes.reshape(-1, 4)
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target["area"] = area
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fields.append("boxes")
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if "input_boxes" in target:
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boxes = target["input_boxes"]
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max_size = torch.as_tensor([w, h], dtype=torch.float32)
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cropped_boxes = boxes - torch.as_tensor([j, i, j, i], dtype=torch.float32)
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cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
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cropped_boxes = cropped_boxes.clamp(min=0)
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target["input_boxes"] = cropped_boxes.reshape(-1, 4)
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if "masks" in target:
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# FIXME should we update the area here if there are no boxes?
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target["masks"] = target["masks"][:, i : i + h, j : j + w]
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fields.append("masks")
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# remove elements for which the boxes or masks that have zero area
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if "boxes" in target or "masks" in target:
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# favor boxes selection when defining which elements to keep
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# this is compatible with previous implementation
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if "boxes" in target:
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cropped_boxes = target["boxes"].reshape(-1, 2, 2)
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keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
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else:
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keep = target["masks"].flatten(1).any(1)
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for field in fields:
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if field in target:
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target[field] = target[field][keep]
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return cropped_image, target
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def hflip(image, target):
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flipped_image = F.hflip(image)
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w, h = image.size
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target = target.copy()
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if "boxes" in target:
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boxes = target["boxes"]
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boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor(
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[-1, 1, -1, 1]
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) + torch.as_tensor([w, 0, w, 0])
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target["boxes"] = boxes
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if "input_boxes" in target:
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boxes = target["input_boxes"]
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boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor(
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[-1, 1, -1, 1]
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) + torch.as_tensor([w, 0, w, 0])
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target["input_boxes"] = boxes
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if "masks" in target:
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target["masks"] = target["masks"].flip(-1)
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if "text_input" in target:
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text_input = (
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target["text_input"]
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.replace("left", "[TMP]")
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.replace("right", "left")
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.replace("[TMP]", "right")
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)
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target["text_input"] = text_input
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return flipped_image, target
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def resize(image, target, size, max_size=None, square=False):
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# size can be min_size (scalar) or (w, h) tuple
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def get_size_with_aspect_ratio(image_size, size, max_size=None):
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w, h = image_size
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if max_size is not None:
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min_original_size = float(min((w, h)))
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max_original_size = float(max((w, h)))
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if max_original_size / min_original_size * size > max_size:
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size = int(round(max_size * min_original_size / max_original_size))
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if (w <= h and w == size) or (h <= w and h == size):
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return (h, w)
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if w < h:
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ow = size
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oh = int(size * h / w)
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else:
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oh = size
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ow = int(size * w / h)
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return (oh, ow)
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def get_size(image_size, size, max_size=None):
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if isinstance(size, (list, tuple)):
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return size[::-1]
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else:
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return get_size_with_aspect_ratio(image_size, size, max_size)
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if square:
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size = size, size
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else:
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size = get_size(image.size, size, max_size)
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rescaled_image = F.resize(image, size)
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if target is None:
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return rescaled_image, None
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ratios = tuple(
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float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size)
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)
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ratio_width, ratio_height = ratios
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target = target.copy()
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if "boxes" in target:
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boxes = target["boxes"]
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scaled_boxes = boxes * torch.as_tensor(
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[ratio_width, ratio_height, ratio_width, ratio_height], dtype=torch.float32
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)
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target["boxes"] = scaled_boxes
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if "input_boxes" in target:
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boxes = target["input_boxes"]
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scaled_boxes = boxes * torch.as_tensor(
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[ratio_width, ratio_height, ratio_width, ratio_height], dtype=torch.float32
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)
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target["input_boxes"] = scaled_boxes
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if "area" in target:
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area = target["area"]
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scaled_area = area * (ratio_width * ratio_height)
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target["area"] = scaled_area
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h, w = size
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target["size"] = torch.tensor([h, w])
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if "masks" in target:
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target["masks"] = (
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interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0]
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> 0.5
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)
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return rescaled_image, target
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def pad(image, target, padding):
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if len(padding) == 2:
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# assumes that we only pad on the bottom right corners
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padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
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else:
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# left, top, right, bottom
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padded_image = F.pad(image, (padding[0], padding[1], padding[2], padding[3]))
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if target is None:
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return padded_image, None
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target = target.copy()
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w, h = padded_image.size
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# should we do something wrt the original size?
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target["size"] = torch.tensor([h, w])
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if "boxes" in target and len(padding) == 4:
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boxes = target["boxes"]
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boxes = boxes + torch.as_tensor(
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[padding[0], padding[1], padding[0], padding[1]], dtype=torch.float32
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)
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target["boxes"] = boxes
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if "input_boxes" in target and len(padding) == 4:
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boxes = target["input_boxes"]
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boxes = boxes + torch.as_tensor(
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[padding[0], padding[1], padding[0], padding[1]], dtype=torch.float32
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)
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target["input_boxes"] = boxes
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if "masks" in target:
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if len(padding) == 2:
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target["masks"] = torch.nn.functional.pad(
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target["masks"], (0, padding[0], 0, padding[1])
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)
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else:
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target["masks"] = torch.nn.functional.pad(
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target["masks"], (padding[0], padding[2], padding[1], padding[3])
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)
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return padded_image, target
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class RandomCrop:
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def __init__(self, size):
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self.size = size
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def __call__(self, img, target):
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region = T.RandomCrop.get_params(img, self.size)
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return crop(img, target, region)
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class RandomSizeCrop:
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def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
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self.min_size = min_size
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self.max_size = max_size
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self.respect_boxes = respect_boxes # if True we can't crop a box out
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def __call__(self, img: PIL.Image.Image, target: dict):
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init_boxes = len(target["boxes"])
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init_boxes_tensor = target["boxes"].clone()
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if self.respect_boxes and init_boxes > 0:
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minW, minH, maxW, maxH = (
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min(img.width, self.min_size),
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min(img.width, self.min_size),
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min(img.width, self.max_size),
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min(img.height, self.max_size),
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)
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minX, minY = (
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target["boxes"][:, 0].max().item() + 10.0,
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target["boxes"][:, 1].max().item() + 10.0,
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)
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minX = min(img.width, minX)
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minY = min(img.height, minY)
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maxX, maxY = (
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target["boxes"][:, 2].min().item() - 10,
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target["boxes"][:, 3].min().item() - 10,
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)
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maxX = max(0.0, maxX)
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maxY = max(0.0, maxY)
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minW = max(minW, minX - maxX)
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minH = max(minH, minY - maxY)
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w = random.uniform(minW, max(minW, maxW))
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h = random.uniform(minH, max(minH, maxH))
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if minX > maxX:
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# i = random.uniform(max(0, minX - w + 1), max(maxX, max(0, minX - w + 1)))
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i = random.uniform(max(0, minX - w), max(maxX, max(0, minX - w)))
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else:
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i = random.uniform(
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max(0, minX - w + 1), max(maxX - 1, max(0, minX - w + 1))
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)
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if minY > maxY:
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# j = random.uniform(max(0, minY - h + 1), max(maxY, max(0, minY - h + 1)))
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j = random.uniform(max(0, minY - h), max(maxY, max(0, minY - h)))
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else:
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j = random.uniform(
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max(0, minY - h + 1), max(maxY - 1, max(0, minY - h + 1))
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)
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result_img, result_target = crop(img, target, [j, i, h, w])
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assert (
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len(result_target["boxes"]) == init_boxes
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), f"img_w={img.width}\timg_h={img.height}\tminX={minX}\tminY={minY}\tmaxX={maxX}\tmaxY={maxY}\tminW={minW}\tminH={minH}\tmaxW={maxW}\tmaxH={maxH}\tw={w}\th={h}\ti={i}\tj={j}\tinit_boxes={init_boxes_tensor}\tresults={result_target['boxes']}"
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return result_img, result_target
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else:
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w = random.randint(self.min_size, min(img.width, self.max_size))
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h = random.randint(self.min_size, min(img.height, self.max_size))
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region = T.RandomCrop.get_params(img, (h, w))
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result_img, result_target = crop(img, target, region)
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return result_img, result_target
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class CenterCrop:
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def __init__(self, size):
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self.size = size
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def __call__(self, img, target):
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image_width, image_height = img.size
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crop_height, crop_width = self.size
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crop_top = int(round((image_height - crop_height) / 2.0))
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crop_left = int(round((image_width - crop_width) / 2.0))
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return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
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class RandomHorizontalFlip:
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def __init__(self, p=0.5):
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self.p = p
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def __call__(self, img, target):
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if random.random() < self.p:
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return hflip(img, target)
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return img, target
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class RandomResize:
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def __init__(self, sizes, max_size=None, square=False):
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if isinstance(sizes, int):
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sizes = (sizes,)
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assert isinstance(sizes, Iterable)
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self.sizes = list(sizes)
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self.max_size = max_size
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self.square = square
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def __call__(self, img, target=None):
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size = random.choice(self.sizes)
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return resize(img, target, size, self.max_size, square=self.square)
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class RandomPad:
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def __init__(self, max_pad):
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self.max_pad = max_pad
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def __call__(self, img, target):
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pad_x = random.randint(0, self.max_pad)
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pad_y = random.randint(0, self.max_pad)
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return pad(img, target, (pad_x, pad_y))
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class PadToSize:
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def __init__(self, size):
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self.size = size
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def __call__(self, img, target):
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w, h = img.size
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pad_x = self.size - w
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pad_y = self.size - h
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assert pad_x >= 0 and pad_y >= 0
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pad_left = random.randint(0, pad_x)
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pad_right = pad_x - pad_left
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pad_top = random.randint(0, pad_y)
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pad_bottom = pad_y - pad_top
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return pad(img, target, (pad_left, pad_top, pad_right, pad_bottom))
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class Identity:
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def __call__(self, img, target):
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return img, target
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class RandomSelect:
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"""
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Randomly selects between transforms1 and transforms2,
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with probability p for transforms1 and (1 - p) for transforms2
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"""
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def __init__(self, transforms1=None, transforms2=None, p=0.5):
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self.transforms1 = transforms1 or Identity()
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self.transforms2 = transforms2 or Identity()
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self.p = p
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def __call__(self, img, target):
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if random.random() < self.p:
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return self.transforms1(img, target)
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return self.transforms2(img, target)
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class ToTensor:
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def __call__(self, img, target):
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return F.to_tensor(img), target
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class RandomErasing:
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def __init__(self, *args, **kwargs):
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self.eraser = T.RandomErasing(*args, **kwargs)
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def __call__(self, img, target):
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return self.eraser(img), target
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class Normalize:
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def __init__(self, mean, std):
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self.mean = mean
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self.std = std
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def __call__(self, image, target=None):
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image = F.normalize(image, mean=self.mean, std=self.std)
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if target is None:
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return image, None
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target = target.copy()
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h, w = image.shape[-2:]
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if "boxes" in target:
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boxes = target["boxes"]
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boxes = box_xyxy_to_cxcywh(boxes)
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boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
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target["boxes"] = boxes
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if "input_boxes" in target:
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boxes = target["input_boxes"]
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boxes = box_xyxy_to_cxcywh(boxes)
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boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
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target["input_boxes"] = boxes
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return image, target
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class RemoveDifficult:
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def __init__(self, enabled=False):
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self.remove_difficult = enabled
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def __call__(self, image, target=None):
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if target is None:
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return image, None
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target = target.copy()
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keep = ~target["iscrowd"].to(torch.bool) | (not self.remove_difficult)
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if "boxes" in target:
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target["boxes"] = target["boxes"][keep]
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target["labels"] = target["labels"][keep]
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target["iscrowd"] = target["iscrowd"][keep]
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return image, target
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class Compose:
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def __init__(self, transforms):
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self.transforms = transforms
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def __call__(self, image, target):
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for t in self.transforms:
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image, target = t(image, target)
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return image, target
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def __repr__(self):
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format_string = self.__class__.__name__ + "("
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for t in self.transforms:
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format_string += "\n"
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format_string += " {0}".format(t)
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format_string += "\n)"
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return format_string
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def get_random_resize_scales(size, min_size, rounded):
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stride = 128 if rounded else 32
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min_size = int(stride * math.ceil(min_size / stride))
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scales = list(range(min_size, size + 1, stride))
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return scales
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def get_random_resize_max_size(size, ratio=5 / 3):
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max_size = round(ratio * size)
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return max_size
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