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#
# 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}.")
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