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"""
Copyright (c) 2024-present Naver Cloud Corp.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
import numpy as np
import torch
from torch.nn import functional as F
from torchvision.transforms.functional import resize, to_pil_image, InterpolationMode
from copy import deepcopy
from typing import Optional, Tuple, List
class ResizeLongestSide:
"""
Resizes images to the longest side 'target_length', as well as provides
methods for resizing coordinates and boxes. Provides methods for
transforming both numpy array and batched torch tensors.
"""
def __init__(self, target_length: int) -> None:
self.target_length = target_length
def apply_image(self, image: np.ndarray) -> np.ndarray:
"""
Expects a numpy array with shape HxWxC in uint8 format.
"""
target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
return np.array(resize(to_pil_image(image), target_size))
def apply_coords(self, coords: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
"""
Expects a numpy array of length 2 in the final dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).astype(float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes(self, boxes: np.ndarray, original_size: Tuple[int, ...]) -> np.ndarray:
"""
Expects a numpy array shape Bx4. Requires the original image size
in (H, W) format.
"""
boxes = self.apply_coords(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
def apply_image_torch(self, image: torch.Tensor) -> torch.Tensor:
"""
Expects batched images with shape BxCxHxW and float format. This
transformation may not exactly match apply_image. apply_image is
the transformation expected by the model.
"""
# Expects an image in BCHW format. May not exactly match apply_image.
target_size = self.get_preprocess_shape(image.shape[2], image.shape[3], self.target_length)
return F.interpolate(
image, target_size, mode="bilinear", align_corners=False, antialias=True
)
def apply_coords_torch(
self, coords: torch.Tensor, original_size: Tuple[int, ...]
) -> torch.Tensor:
"""
Expects a torch tensor with length 2 in the last dimension. Requires the
original image size in (H, W) format.
"""
old_h, old_w = original_size
new_h, new_w = self.get_preprocess_shape(
original_size[0], original_size[1], self.target_length
)
coords = deepcopy(coords).to(torch.float)
coords[..., 0] = coords[..., 0] * (new_w / old_w)
coords[..., 1] = coords[..., 1] * (new_h / old_h)
return coords
def apply_boxes_torch(
self, boxes: torch.Tensor, original_size: Tuple[int, ...]
) -> torch.Tensor:
"""
Expects a torch tensor with shape Bx4. Requires the original image
size in (H, W) format.
"""
boxes = self.apply_coords_torch(boxes.reshape(-1, 2, 2), original_size)
return boxes.reshape(-1, 4)
def apply_mask(self, image: np.ndarray) -> np.ndarray:
"""
Expects a numpy array with shape HxWxC in uint8 format.
"""
target_size = self.get_preprocess_shape(image.shape[0], image.shape[1], self.target_length)
return np.array(resize(to_pil_image(image), target_size, interpolation=InterpolationMode.NEAREST))
@staticmethod
def get_preprocess_shape(oldh: int, oldw: int, long_side_length: int) -> Tuple[int, int]:
"""
Compute the output size given input size and target long side length.
"""
scale = long_side_length * 1.0 / max(oldh, oldw)
newh, neww = oldh * scale, oldw * scale
neww = int(neww + 0.5)
newh = int(newh + 0.5)
return (newh, neww)
def remove_prefix(text, prefix):
if text.startswith(prefix):
return text[len(prefix) :]
return text
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, is_ddp):
self.is_ddp = is_ddp
self.reset()
def reset(self):
self.val = 0.0
self.avg = 0.0
self.sum = 0.0
self.count = 0.0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / (self.count + 1e-5)
def synch(self, device):
if self.is_ddp is False:
return
_sum = torch.tensor(self.sum).to(device)
_count = torch.tensor(self.count).to(device)
torch.distributed.reduce(_sum, dst=0)
torch.distributed.reduce(_count, dst=0)
if torch.distributed.get_rank() == 0:
self.sum = _sum.item()
self.count = _count.item()
self.avg = self.sum / (self.count + 1e-5)
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