groundingdino/util/utils.py

import argparse
import json
import warnings
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Dict, List

import numpy as np
import torch
from transformers import AutoTokenizer

from groundingdino.util.slconfig import SLConfig


def slprint(x, name="x"):
    if isinstance(x, (torch.Tensor, np.ndarray)):
        print(f"{name}.shape:", x.shape)
    elif isinstance(x, (tuple, list)):
        print("type x:", type(x))
        for i in range(min(10, len(x))):
            slprint(x[i], f"{name}[{i}]")
    elif isinstance(x, dict):
        for k, v in x.items():
            slprint(v, f"{name}[{k}]")
    else:
        print(f"{name}.type:", type(x))


def clean_state_dict(state_dict):
    new_state_dict = OrderedDict()
    for k, v in state_dict.items():
        if k[:7] == "module.":
            k = k[7:]  # remove `module.`
        new_state_dict[k] = v
    return new_state_dict


def renorm(
    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
) -> torch.FloatTensor:
    # img: tensor(3,H,W) or tensor(B,3,H,W)
    # return: same as img
    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
    if img.dim() == 3:
        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
            img.size(0),
            str(img.size()),
        )
        img_perm = img.permute(1, 2, 0)
        mean = torch.Tensor(mean)
        std = torch.Tensor(std)
        img_res = img_perm * std + mean
        return img_res.permute(2, 0, 1)
    else:  # img.dim() == 4
        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
            img.size(1),
            str(img.size()),
        )
        img_perm = img.permute(0, 2, 3, 1)
        mean = torch.Tensor(mean)
        std = torch.Tensor(std)
        img_res = img_perm * std + mean
        return img_res.permute(0, 3, 1, 2)


class CocoClassMapper:
    def __init__(self) -> None:
        self.category_map_str = {
            "1": 1,
            "2": 2,
            "3": 3,
            "4": 4,
            "5": 5,
            "6": 6,
            "7": 7,
            "8": 8,
            "9": 9,
            "10": 10,
            "11": 11,
            "13": 12,
            "14": 13,
            "15": 14,
            "16": 15,
            "17": 16,
            "18": 17,
            "19": 18,
            "20": 19,
            "21": 20,
            "22": 21,
            "23": 22,
            "24": 23,
            "25": 24,
            "27": 25,
            "28": 26,
            "31": 27,
            "32": 28,
            "33": 29,
            "34": 30,
            "35": 31,
            "36": 32,
            "37": 33,
            "38": 34,
            "39": 35,
            "40": 36,
            "41": 37,
            "42": 38,
            "43": 39,
            "44": 40,
            "46": 41,
            "47": 42,
            "48": 43,
            "49": 44,
            "50": 45,
            "51": 46,
            "52": 47,
            "53": 48,
            "54": 49,
            "55": 50,
            "56": 51,
            "57": 52,
            "58": 53,
            "59": 54,
            "60": 55,
            "61": 56,
            "62": 57,
            "63": 58,
            "64": 59,
            "65": 60,
            "67": 61,
            "70": 62,
            "72": 63,
            "73": 64,
            "74": 65,
            "75": 66,
            "76": 67,
            "77": 68,
            "78": 69,
            "79": 70,
            "80": 71,
            "81": 72,
            "82": 73,
            "84": 74,
            "85": 75,
            "86": 76,
            "87": 77,
            "88": 78,
            "89": 79,
            "90": 80,
        }
        self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
        self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}

    def origin2compact(self, idx):
        return self.origin2compact_mapper[int(idx)]

    def compact2origin(self, idx):
        return self.compact2origin_mapper[int(idx)]


def to_device(item, device):
    if isinstance(item, torch.Tensor):
        return item.to(device)
    elif isinstance(item, list):
        return [to_device(i, device) for i in item]
    elif isinstance(item, dict):
        return {k: to_device(v, device) for k, v in item.items()}
    else:
        raise NotImplementedError(
            "Call Shilong if you use other containers! type: {}".format(type(item))
        )


#
def get_gaussian_mean(x, axis, other_axis, softmax=True):
    """

    Args:
        x (float): Input images(BxCxHxW)
        axis (int): The index for weighted mean
        other_axis (int): The other index

    Returns: weighted index for axis, BxC

    """
    mat2line = torch.sum(x, axis=other_axis)
    # mat2line = mat2line / mat2line.mean() * 10
    if softmax:
        u = torch.softmax(mat2line, axis=2)
    else:
        u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)
    size = x.shape[axis]
    ind = torch.linspace(0, 1, size).to(x.device)
    batch = x.shape[0]
    channel = x.shape[1]
    index = ind.repeat([batch, channel, 1])
    mean_position = torch.sum(index * u, dim=2)
    return mean_position


def get_expected_points_from_map(hm, softmax=True):
    """get_gaussian_map_from_points
        B,C,H,W -> B,N,2 float(0, 1) float(0, 1)
        softargmax function

    Args:
        hm (float): Input images(BxCxHxW)

    Returns:
        weighted index for axis, BxCx2. float between 0 and 1.

    """
    # hm = 10*hm
    B, C, H, W = hm.shape
    y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax)  # B,C
    x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax)  # B,C
    # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)
    return torch.stack([x_mean, y_mean], dim=2)


# Positional encoding (section 5.1)
# borrow from nerf
class Embedder:
    def __init__(self, **kwargs):
        self.kwargs = kwargs
        self.create_embedding_fn()

    def create_embedding_fn(self):
        embed_fns = []
        d = self.kwargs["input_dims"]
        out_dim = 0
        if self.kwargs["include_input"]:
            embed_fns.append(lambda x: x)
            out_dim += d

        max_freq = self.kwargs["max_freq_log2"]
        N_freqs = self.kwargs["num_freqs"]

        if self.kwargs["log_sampling"]:
            freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)
        else:
            freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)

        for freq in freq_bands:
            for p_fn in self.kwargs["periodic_fns"]:
                embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
                out_dim += d

        self.embed_fns = embed_fns
        self.out_dim = out_dim

    def embed(self, inputs):
        return torch.cat([fn(inputs) for fn in self.embed_fns], -1)


def get_embedder(multires, i=0):
    import torch.nn as nn

    if i == -1:
        return nn.Identity(), 3

    embed_kwargs = {
        "include_input": True,
        "input_dims": 3,
        "max_freq_log2": multires - 1,
        "num_freqs": multires,
        "log_sampling": True,
        "periodic_fns": [torch.sin, torch.cos],
    }

    embedder_obj = Embedder(**embed_kwargs)
    embed = lambda x, eo=embedder_obj: eo.embed(x)
    return embed, embedder_obj.out_dim


class APOPMeter:
    def __init__(self) -> None:
        self.tp = 0
        self.fp = 0
        self.tn = 0
        self.fn = 0

    def update(self, pred, gt):
        """
        Input:
            pred, gt: Tensor()
        """
        assert pred.shape == gt.shape
        self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()
        self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()
        self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()
        self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()

    def update_cm(self, tp, fp, tn, fn):
        self.tp += tp
        self.fp += fp
        self.tn += tn
        self.tn += fn


def inverse_sigmoid(x, eps=1e-5):
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1 / x2)


def get_raw_dict(args):
    """
    return the dicf contained in args.

    e.g:
        >>> with open(path, 'w') as f:
                json.dump(get_raw_dict(args), f, indent=2)
    """
    if isinstance(args, argparse.Namespace):
        return vars(args)
    elif isinstance(args, dict):
        return args
    elif isinstance(args, SLConfig):
        return args._cfg_dict
    else:
        raise NotImplementedError("Unknown type {}".format(type(args)))


def stat_tensors(tensor):
    assert tensor.dim() == 1
    tensor_sm = tensor.softmax(0)
    entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()

    return {
        "max": tensor.max(),
        "min": tensor.min(),
        "mean": tensor.mean(),
        "var": tensor.var(),
        "std": tensor.var() ** 0.5,
        "entropy": entropy,
    }


class NiceRepr:
    """Inherit from this class and define ``__nice__`` to "nicely" print your
    objects.

    Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
    Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
    If the inheriting class has a ``__len__``, method then the default
    ``__nice__`` method will return its length.

    Example:
        >>> class Foo(NiceRepr):
        ...    def __nice__(self):
        ...        return 'info'
        >>> foo = Foo()
        >>> assert str(foo) == '<Foo(info)>'
        >>> assert repr(foo).startswith('<Foo(info) at ')

    Example:
        >>> class Bar(NiceRepr):
        ...    pass
        >>> bar = Bar()
        >>> import pytest
        >>> with pytest.warns(None) as record:
        >>>     assert 'object at' in str(bar)
        >>>     assert 'object at' in repr(bar)

    Example:
        >>> class Baz(NiceRepr):
        ...    def __len__(self):
        ...        return 5
        >>> baz = Baz()
        >>> assert str(baz) == '<Baz(5)>'
    """

    def __nice__(self):
        """str: a "nice" summary string describing this module"""
        if hasattr(self, "__len__"):
            # It is a common pattern for objects to use __len__ in __nice__
            # As a convenience we define a default __nice__ for these objects
            return str(len(self))
        else:
            # In all other cases force the subclass to overload __nice__
            raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")

    def __repr__(self):
        """str: the string of the module"""
        try:
            nice = self.__nice__()
            classname = self.__class__.__name__
            return f"<{classname}({nice}) at {hex(id(self))}>"
        except NotImplementedError as ex:
            warnings.warn(str(ex), category=RuntimeWarning)
            return object.__repr__(self)

    def __str__(self):
        """str: the string of the module"""
        try:
            classname = self.__class__.__name__
            nice = self.__nice__()
            return f"<{classname}({nice})>"
        except NotImplementedError as ex:
            warnings.warn(str(ex), category=RuntimeWarning)
            return object.__repr__(self)


def ensure_rng(rng=None):
    """Coerces input into a random number generator.

    If the input is None, then a global random state is returned.

    If the input is a numeric value, then that is used as a seed to construct a
    random state. Otherwise the input is returned as-is.

    Adapted from [1]_.

    Args:
        rng (int | numpy.random.RandomState | None):
            if None, then defaults to the global rng. Otherwise this can be an
            integer or a RandomState class
    Returns:
        (numpy.random.RandomState) : rng -
            a numpy random number generator

    References:
        .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270  # noqa: E501
    """

    if rng is None:
        rng = np.random.mtrand._rand
    elif isinstance(rng, int):
        rng = np.random.RandomState(rng)
    else:
        rng = rng
    return rng


def random_boxes(num=1, scale=1, rng=None):
    """Simple version of ``kwimage.Boxes.random``

    Returns:
        Tensor: shape (n, 4) in x1, y1, x2, y2 format.

    References:
        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390

    Example:
        >>> num = 3
        >>> scale = 512
        >>> rng = 0
        >>> boxes = random_boxes(num, scale, rng)
        >>> print(boxes)
        tensor([[280.9925, 278.9802, 308.6148, 366.1769],
                [216.9113, 330.6978, 224.0446, 456.5878],
                [405.3632, 196.3221, 493.3953, 270.7942]])
    """
    rng = ensure_rng(rng)

    tlbr = rng.rand(num, 4).astype(np.float32)

    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])

    tlbr[:, 0] = tl_x * scale
    tlbr[:, 1] = tl_y * scale
    tlbr[:, 2] = br_x * scale
    tlbr[:, 3] = br_y * scale

    boxes = torch.from_numpy(tlbr)
    return boxes


class ModelEma(torch.nn.Module):
    def __init__(self, model, decay=0.9997, device=None):
        super(ModelEma, self).__init__()
        # make a copy of the model for accumulating moving average of weights
        self.module = deepcopy(model)
        self.module.eval()

        # import ipdb; ipdb.set_trace()

        self.decay = decay
        self.device = device  # perform ema on different device from model if set
        if self.device is not None:
            self.module.to(device=device)

    def _update(self, model, update_fn):
        with torch.no_grad():
            for ema_v, model_v in zip(
                self.module.state_dict().values(), model.state_dict().values()
            ):
                if self.device is not None:
                    model_v = model_v.to(device=self.device)
                ema_v.copy_(update_fn(ema_v, model_v))

    def update(self, model):
        self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)

    def set(self, model):
        self._update(model, update_fn=lambda e, m: m)


class BestMetricSingle:
    def __init__(self, init_res=0.0, better="large") -> None:
        self.init_res = init_res
        self.best_res = init_res
        self.best_ep = -1

        self.better = better
        assert better in ["large", "small"]

    def isbetter(self, new_res, old_res):
        if self.better == "large":
            return new_res > old_res
        if self.better == "small":
            return new_res < old_res

    def update(self, new_res, ep):
        if self.isbetter(new_res, self.best_res):
            self.best_res = new_res
            self.best_ep = ep
            return True
        return False

    def __str__(self) -> str:
        return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep)

    def __repr__(self) -> str:
        return self.__str__()

    def summary(self) -> dict:
        return {
            "best_res": self.best_res,
            "best_ep": self.best_ep,
        }


class BestMetricHolder:
    def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
        self.best_all = BestMetricSingle(init_res, better)
        self.use_ema = use_ema
        if use_ema:
            self.best_ema = BestMetricSingle(init_res, better)
            self.best_regular = BestMetricSingle(init_res, better)

    def update(self, new_res, epoch, is_ema=False):
        """
        return if the results is the best.
        """
        if not self.use_ema:
            return self.best_all.update(new_res, epoch)
        else:
            if is_ema:
                self.best_ema.update(new_res, epoch)
                return self.best_all.update(new_res, epoch)
            else:
                self.best_regular.update(new_res, epoch)
                return self.best_all.update(new_res, epoch)

    def summary(self):
        if not self.use_ema:
            return self.best_all.summary()

        res = {}
        res.update({f"all_{k}": v for k, v in self.best_all.summary().items()})
        res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()})
        res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()})
        return res

    def __repr__(self) -> str:
        return json.dumps(self.summary(), indent=2)

    def __str__(self) -> str:
        return self.__repr__()


def targets_to(targets: List[Dict[str, Any]], device):
    """Moves the target dicts to the given device."""
    excluded_keys = [
        "questionId",
        "tokens_positive",
        "strings_positive",
        "tokens",
        "dataset_name",
        "sentence_id",
        "original_img_id",
        "nb_eval",
        "task_id",
        "original_id",
        "token_span",
        "caption",
        "dataset_type",
    ]
    return [
        {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
    ]


def get_phrases_from_posmap(
    posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer
):
    assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
    if posmap.dim() == 1:
        non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
        token_ids = [tokenized["input_ids"][i] for i in non_zero_idx]
        return tokenizer.decode(token_ids)
    else:
        raise NotImplementedError("posmap must be 1-dim")