mmdetection/projects/XDecoder/xdecoder/utils.py

import logging
from contextlib import contextmanager
from functools import wraps

import torch
from mmcv.cnn.bricks.wrappers import obsolete_torch_version
from torch.nn import functional as F

TORCH_VERSION = tuple(int(x) for x in torch.__version__.split('.')[:2])


def is_lower_torch_version(version=(1, 10)):
    """Check if the pytorch version is lower than "version."""
    return obsolete_torch_version(TORCH_VERSION, version)


@contextmanager
def _ignore_torch_cuda_oom():
    """A context which ignores CUDA OOM exception from pytorch."""
    try:
        yield
    except RuntimeError as e:
        if 'CUDA out of memory. ' in str(e):
            pass
        else:
            raise


def retry_if_cuda_oom(func):
    """Makes a function retry itself after encountering pytorch's CUDA OOM
    error. It will first retry after calling `torch.cuda.empty_cache()`.

    If that still fails, it will then retry by trying to convert inputs
    to CPUs. In this case, it expects the function to dispatch to CPU
    implementation. The return values may become CPU tensors as well
    and it's user's responsibility to convert it back to CUDA tensor
    if needed.

    Args:
        func: a stateless callable that takes tensor-like objects as arguments

    Returns:
        a callable which retries `func` if OOM is encountered.

    Examples:
    ::
        output = retry_if_cuda_oom(some_torch_function)(input1, input2)
        # output may be on CPU even if inputs are on GPU

    Note:
        1. When converting inputs to CPU, it will only
           look at each argument and check if it has `.device`
           and `.to` for conversion. Nested structures of tensors
           are not supported.

        2. Since the function might be called more than once, it has to be
           stateless.
    """

    def maybe_to_cpu(x):
        try:
            like_gpu_tensor = x.device.type == 'cuda' and hasattr(x, 'to')
        except AttributeError:
            like_gpu_tensor = False
        if like_gpu_tensor:
            return x.to(device='cpu')
        else:
            return x

    @wraps(func)
    def wrapped(*args, **kwargs):
        with _ignore_torch_cuda_oom():
            return func(*args, **kwargs)

        # Clear cache and retry
        torch.cuda.empty_cache()
        with _ignore_torch_cuda_oom():
            return func(*args, **kwargs)

        # Try on CPU. This slows down the code significantly,
        # therefore print a notice.
        logger = logging.getLogger(__name__)
        logger.info(
            'Attempting to copy inputs of {} to CPU due to CUDA OOM'.format(
                str(func)[0:5]))
        new_args = (maybe_to_cpu(x) for x in args)
        new_kwargs = {k: maybe_to_cpu(v) for k, v in kwargs.items()}
        return func(*new_args, **new_kwargs)

    return wrapped


def sem_seg_postprocess(result, img_size, output_height, output_width):
    """Return semantic segmentation predictions in the original resolution.

    The input images are often resized when entering semantic segmentor.
    Moreover, in same cases, they also padded inside segmentor to be
    divisible by maximum network stride. As a result, we often need
    the predictions of the segmentor in a different resolution from
    its inputs.

    Args:
        result (Tensor): semantic segmentation prediction logits.
            A tensor of shape (C, H, W), where C is the number of classes,
            and H, W are the height and width of the prediction.
        img_size (tuple): image size that segmentor is taking as input.
        output_height, output_width: the desired output resolution.

    Returns:
        semantic segmentation prediction (Tensor): A tensor of the shape
            (C, output_height, output_width) that contains per-pixel
            soft predictions.
    """
    result = result[:, :img_size[0], :img_size[1]].expand(1, -1, -1, -1)
    if is_lower_torch_version():
        result = F.interpolate(
            result,
            size=(output_height, output_width),
            mode='bicubic',
            align_corners=False)[0]
    else:
        result = F.interpolate(
            result,
            size=(output_height, output_width),
            mode='bicubic',
            align_corners=False,
            antialias=True)[0]
    return result


def get_prompt_templates():
    prompt_templates = [
        '{}.',
        'a photo of a {}.',
        'a bad photo of a {}.',
        'a photo of many {}.',
        'a sculpture of a {}.',
        'a photo of the hard to see {}.',
        'a low resolution photo of the {}.',
        'a rendering of a {}.',
        'graffiti of a {}.',
        'a bad photo of the {}.',
        'a cropped photo of the {}.',
        'a tattoo of a {}.',
        'the embroidered {}.',
        'a photo of a hard to see {}.',
        'a bright photo of a {}.',
        'a photo of a clean {}.',
        'a photo of a dirty {}.',
        'a dark photo of the {}.',
        'a drawing of a {}.',
        'a photo of my {}.',
        'the plastic {}.',
        'a photo of the cool {}.',
        'a close-up photo of a {}.',
        'a black and white photo of the {}.',
        'a painting of the {}.',
        'a painting of a {}.',
        'a pixelated photo of the {}.',
        'a sculpture of the {}.',
        'a bright photo of the {}.',
        'a cropped photo of a {}.',
        'a plastic {}.',
        'a photo of the dirty {}.',
        'a jpeg corrupted photo of a {}.',
        'a blurry photo of the {}.',
        'a photo of the {}.',
        'a good photo of the {}.',
        'a rendering of the {}.',
        'a {} in a video game.',
        'a photo of one {}.',
        'a doodle of a {}.',
        'a close-up photo of the {}.',
        'the origami {}.',
        'the {} in a video game.',
        'a sketch of a {}.',
        'a doodle of the {}.',
        'a origami {}.',
        'a low resolution photo of a {}.',
        'the toy {}.',
        'a rendition of the {}.',
        'a photo of the clean {}.',
        'a photo of a large {}.',
        'a rendition of a {}.',
        'a photo of a nice {}.',
        'a photo of a weird {}.',
        'a blurry photo of a {}.',
        'a cartoon {}.',
        'art of a {}.',
        'a sketch of the {}.',
        'a embroidered {}.',
        'a pixelated photo of a {}.',
        'itap of the {}.',
        'a jpeg corrupted photo of the {}.',
        'a good photo of a {}.',
        'a plushie {}.',
        'a photo of the nice {}.',
        'a photo of the small {}.',
        'a photo of the weird {}.',
        'the cartoon {}.',
        'art of the {}.',
        'a drawing of the {}.',
        'a photo of the large {}.',
        'a black and white photo of a {}.',
        'the plushie {}.',
        'a dark photo of a {}.',
        'itap of a {}.',
        'graffiti of the {}.',
        'a toy {}.',
        'itap of my {}.',
        'a photo of a cool {}.',
        'a photo of a small {}.',
        'a tattoo of the {}.',
    ]
    return prompt_templates