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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
this file is borrowed from DETR repo: https://github.com/facebookresearch/detr/blob/main/util/misc.py
"""
import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from packaging import version
from typing import Optional, List
import torch
import torch.distributed as dist
from torch import Tensor
# needed due to empty tensor bug in pytorch and torchvision 0.5
import torchvision
if version.parse(torchvision.__version__) < version.parse('0.7'):
from torchvision.ops import _new_empty_tensor
from torchvision.ops.misc import _output_size
class NestedTensor(object):
def __init__(self, tensors, mask: Optional[Tensor]):
self.tensors = tensors
self.mask = mask
def to(self, device):
# type: (Device) -> NestedTensor # noqa
cast_tensor = self.tensors.to(device)
mask = self.mask
if mask is not None:
assert mask is not None
cast_mask = mask.to(device)
else:
cast_mask = None
return NestedTensor(cast_tensor, cast_mask)
def decompose(self):
return self.tensors, self.mask
def __repr__(self):
return str(self.tensors)
def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
# TODO make this more general
if tensor_list[0].ndim == 3:
if torchvision._is_tracing():
# nested_tensor_from_tensor_list() does not export well to ONNX
# call _onnx_nested_tensor_from_tensor_list() instead
return _onnx_nested_tensor_from_tensor_list(tensor_list)
# 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)
def add_mask(tracklets):
'''
input the pieces of tracklets, add the mask overit, the padded
positions are set to be True, False for where box exists
'''
p, l = tracklets.shape[:2]
sum_cord = torch.sum(tracklets[:,:,1:4], dim=2)
mask = (sum_cord==0)
return NestedTensor(tracklets, mask) |