src/dinov3/eval/detection/util/misc.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.

# ------------------------------------------------------------------------
# Plain-DETR
# Copyright (c) 2023 Xi'an Jiaotong University & Microsoft Research Asia.
# Licensed under The MIT License [see LICENSE for details]
# ------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# ------------------------------------------------------------------------

"""
Misc functions, including distributed helpers.

Mostly copy-paste from torchvision references.
"""
import copy
from typing import List, Optional

import dinov3.distributed as distributed
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F

# needed due to empty tensor bug in pytorch and torchvision 0.5
import torchvision
from torch import Tensor


def reduce_dict(input_dict, average=True):
    """
    Args:
        input_dict (dict): all the values will be reduced
        average (bool): whether to do average or sum
    Reduce the values in the dictionary from all processes so that all processes
    have the averaged results. Returns a dict with the same fields as
    input_dict, after reduction.
    """
    world_size = distributed.get_world_size()
    if world_size < 2:
        return input_dict
    with torch.no_grad():
        names = []
        values = []
        # sort the keys so that they are consistent across processes
        for k in sorted(input_dict.keys()):
            names.append(k)
            values.append(input_dict[k])
        values = torch.stack(values, dim=0)
        dist.all_reduce(values)
        if average:
            values /= world_size
        reduced_dict = {k: v for k, v in zip(names, values)}
    return reduced_dict


def collate_fn(batch):
    batch = list(zip(*batch))
    batch[0] = nested_tensor_from_tensor_list(batch[0])
    return tuple(batch)


def _max_by_axis(the_list):
    # type: (List[List[int]]) -> List[int]
    maxes = the_list[0]
    for sublist in the_list[1:]:
        for index, item in enumerate(sublist):
            maxes[index] = max(maxes[index], item)
    return maxes


def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
    # TODO make this more general
    if tensor_list[0].ndim == 3:
        # TODO make it support different-sized images
        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
        batch_shape = [len(tensor_list)] + max_size
        b, c, h, w = batch_shape
        dtype = tensor_list[0].dtype
        device = tensor_list[0].device
        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
        for img, pad_img, m in zip(tensor_list, tensor, mask):
            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
            m[: img.shape[1], : img.shape[2]] = False
    else:
        raise ValueError("not supported")
    return NestedTensor(tensor, mask)


class NestedTensor(object):
    def __init__(self, tensors, mask: Optional[Tensor]):
        self.tensors = tensors
        self.mask = mask

    def to(self, device, non_blocking=False):
        cast_tensor = self.tensors.to(device, non_blocking=non_blocking)
        mask = self.mask
        if mask is not None:
            assert mask is not None
            cast_mask = mask.to(device, non_blocking=non_blocking)
        else:
            cast_mask = None
        return NestedTensor(cast_tensor, cast_mask)

    def record_stream(self, *args, **kwargs):
        self.tensors.record_stream(*args, **kwargs)
        if self.mask is not None:
            self.mask.record_stream(*args, **kwargs)

    def decompose(self):
        return self.tensors, self.mask

    def __repr__(self):
        return str(self.tensors)

    def __len__(self):
        return len(self.tensors)


@torch.no_grad()
def accuracy(output, target, topk=(1,)):
    """Computes the precision@k for the specified values of k"""
    if target.numel() == 0:
        return [torch.zeros([], device=output.device)]
    maxk = max(topk)
    batch_size = target.size(0)

    _, pred = output.topk(maxk, 1, True, True)
    pred = pred.t()
    correct = pred.eq(target.view(1, -1).expand_as(pred))

    res = []
    for k in topk:
        correct_k = correct[:k].view(-1).float().sum(0)
        res.append(correct_k.mul_(100.0 / batch_size))
    return res


def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
    """
    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
    This will eventually be supported natively by PyTorch, and this
    class can go away.
    """
    return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)


def get_total_grad_norm(parameters, norm_type=2):
    parameters = list(filter(lambda p: p.grad is not None, parameters))
    norm_type = float(norm_type)
    device = parameters[0].grad.device
    total_norm = torch.norm(
        torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),
        norm_type,
    )
    return total_norm


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 match_name_keywords(n, name_keywords):
    out = False
    for b in name_keywords:
        if b in n:
            out = True
            break
    return out


def get_param_dict(model, args, return_name=False, use_layerwise_decay=False):
    # sanity check: a variable could not match backbone_names and linear_proj_names at the same time
    for n, p in model.named_parameters():
        if match_name_keywords(n, args.lr_backbone_names) and match_name_keywords(n, args.lr_linear_proj_names):
            raise ValueError

    param_dicts = [
        {
            "params": [
                p if not return_name else n
                for n, p in model.named_parameters()
                if not match_name_keywords(n, args.lr_backbone_names)
                and not match_name_keywords(n, args.lr_linear_proj_names)
                and not match_name_keywords(n, args.wd_norm_names)
                and p.requires_grad
            ],
            "lr": args.lr,
            "weight_decay": args.weight_decay,
        },
        {
            "params": [
                p if not return_name else n
                for n, p in model.named_parameters()
                if match_name_keywords(n, args.lr_backbone_names)
                and not match_name_keywords(n, args.lr_linear_proj_names)
                and not match_name_keywords(n, args.wd_norm_names)
                and p.requires_grad
            ],
            "lr": args.lr_backbone,
            "weight_decay": args.weight_decay,
        },
        {
            "params": [
                p if not return_name else n
                for n, p in model.named_parameters()
                if not match_name_keywords(n, args.lr_backbone_names)
                and match_name_keywords(n, args.lr_linear_proj_names)
                and not match_name_keywords(n, args.wd_norm_names)
                and p.requires_grad
            ],
            "lr": args.lr * args.lr_linear_proj_mult,
            "weight_decay": args.weight_decay,
        },
        {
            "params": [
                p if not return_name else n
                for n, p in model.named_parameters()
                if not match_name_keywords(n, args.lr_backbone_names)
                and not match_name_keywords(n, args.lr_linear_proj_names)
                and match_name_keywords(n, args.wd_norm_names)
                and p.requires_grad
            ],
            "lr": args.lr,
            "weight_decay": args.weight_decay * args.wd_norm_mult,
        },
        {
            "params": [
                p if not return_name else n
                for n, p in model.named_parameters()
                if match_name_keywords(n, args.lr_backbone_names)
                and not match_name_keywords(n, args.lr_linear_proj_names)
                and match_name_keywords(n, args.wd_norm_names)
                and p.requires_grad
            ],
            "lr": args.lr_backbone,
            "weight_decay": args.weight_decay * args.wd_norm_mult,
        },
        {
            "params": [
                p if not return_name else n
                for n, p in model.named_parameters()
                if not match_name_keywords(n, args.lr_backbone_names)
                and match_name_keywords(n, args.lr_linear_proj_names)
                and match_name_keywords(n, args.wd_norm_names)
                and p.requires_grad
            ],
            "lr": args.lr * args.lr_linear_proj_mult,
            "weight_decay": args.weight_decay * args.wd_norm_mult,
        },
    ]
    return param_dicts


def _get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])


def _get_activation_fn(activation):
    """Return an activation function given a string"""
    if activation == "relu":
        return F.relu
    if activation == "gelu":
        return F.gelu
    if activation == "glu":
        return F.glu
    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")