functions.py

import os
import subprocess
import time
import datetime
import pickle
import numpy as np
from packaging import version
from collections import defaultdict, deque
from scipy.optimize import linear_sum_assignment
from typing import Optional, List

import torch
from torch import nn
from torch import Tensor
import torch.distributed as dist
from torchvision.ops.boxes import box_area
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


def is_empty_tensor(tensor):
    return tensor.numel() == 0


def normalize_ct_data(data_array, hu_min=-200, hu_max=800):

    clipped_array = np.clip(data_array, hu_min, hu_max)
    normalized_array = (clipped_array - hu_min) / (hu_max - hu_min)
    return normalized_array


def _3d_cubes_selection(input_volume, cube_size, num_cubes, step, batch_size):

    b, n_l, n_h, n_w = input_volume.shape
    centers = [step // 2 + step * i - 1 for i in range(num_cubes)]
    output_cubes = torch.zeros((batch_size, len(centers), cube_size, cube_size, cube_size))

    for i, center in enumerate(centers):
        start = center - cube_size // 2
        end = start + cube_size
        set_start, set_end = 0, cube_size
        if start < 0:
            set_start -= start
            start = 0
        if end > n_l:
            set_end = cube_size - (end - n_l)
            end = n_l
        cut_start, cut_end = int(n_h / 2 - cube_size // 2), int(n_h / 2 + cube_size // 2 + 1)
        output_cubes[:, i, set_start: set_end, :, :] = input_volume[:, start:end,
                                                                    cut_start: cut_end,
                                                                    cut_start: cut_end]
    return output_cubes


def number_parameters(Net, type_size=8):
    para = sum([np.prod(list(p.size())) for p in Net.parameters()])
    return para / 1024 * type_size / 1024


def gradient_preference(*input_tensors):
    numpy_arrays = []
    for tensor in input_tensors:
        if tensor.requires_grad:
            numpy_arrays.append(tensor.detach())
        else:
            numpy_arrays.append(tensor)
    return numpy_arrays


class HungarianMatcher(nn.Module):
    def __init__(self, cost_class: float = 1, cost_bbox: float = 1, cost_giou: float = 1):
        super().__init__()

        self.cost_class = cost_class
        self.cost_bbox = cost_bbox
        self.cost_giou = cost_giou
        assert cost_class != 0 or cost_bbox != 0 or cost_giou != 0, "all costs cant be 0"

    def forward(self, outputs, targets):

        bs, num_queries = outputs["pred_logits"].shape[:2]

        out_prob = outputs["pred_logits"].flatten(0, 1).softmax(-1)
        out_bbox = outputs["pred_boxes"].flatten(0, 1)
        tgt_ids = torch.cat([v["labels"] for v in targets])
        tgt_bbox = torch.cat([v["boxes"] for v in targets])
        tgt_ids = tgt_ids.to(dtype=torch.long)

        numpy_array_group = gradient_preference(out_prob, out_bbox, tgt_ids, tgt_bbox)
        out_prob, out_bbox, tgt_ids, tgt_bbox = numpy_array_group[0], numpy_array_group[1],\
                                                numpy_array_group[2], numpy_array_group[3]

        if tgt_ids.numel() == 0:
            return [(torch.empty(0, dtype=torch.int64), torch.empty(0, dtype=torch.int64)) for _ in range(bs)]

        cost_class = -out_prob[:, tgt_ids]
        cost_bbox = torch.cdist(out_bbox, tgt_bbox, p=1)
        cost_giou = -generalized_box_iou(box_cxcywh_to_xyxy(out_bbox), box_cxcywh_to_xyxy(tgt_bbox))

        C = self.cost_bbox * cost_bbox + self.cost_class * cost_class + self.cost_giou * cost_giou
        C = C.view(bs, num_queries, -1).cpu()

        sizes = [len(v["boxes"]) for v in targets]
        indices = [linear_sum_assignment(c[i]) for i, c in enumerate(C.split(sizes, -1))]
        return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices]


def box_lastdim_expansion(data):

    if is_empty_tensor(data) == True:
        return data

    expanded_data = data.unsqueeze(-2).expand(*data.shape[:-1], 2, 2).reshape(*data.shape[:-1], 4)
    return expanded_data[..., [0, 2, 1, 3]]


def boxes_dimension_expansion(data, dtype='outputs'):

    if dtype == 'outputs':
        data["pred_boxes"] = box_lastdim_expansion(data["pred_boxes"])
    if dtype == 'targets':
        for tmp_data in data:
            tmp_data["boxes"] = box_lastdim_expansion(tmp_data["boxes"])
    return data


class SmoothedValue(object):
    def __init__(self, window_size=20, fmt=None):
        if fmt is None:
            fmt = "{median:.4f} ({global_avg:.4f})"
        self.deque = deque(maxlen=window_size)
        self.total = 0.0
        self.count = 0
        self.fmt = fmt

    def update(self, value, n=1):
        self.deque.append(value)
        self.count += n
        self.total += value * n

    def synchronize_between_processes(self):

        if not is_dist_avail_and_initialized():
            return
        t = torch.tensor([self.count, self.total], dtype=torch.float64, device='cuda')
        dist.barrier()
        dist.all_reduce(t)
        t = t.tolist()
        self.count = int(t[0])
        self.total = t[1]

    def median(self):
        d = torch.tensor(list(self.deque))
        return d.median().item()

    def avg(self):
        d = torch.tensor(list(self.deque), dtype=torch.float32)
        return d.mean().item()

    def global_avg(self):
        return self.total / self.count

    def max(self):
        return max(self.deque)

    def value(self):
        return self.deque[-1]

    def __str__(self):
        return self.fmt.format(
            median=self.median,
            avg=self.avg,
            global_avg=self.global_avg,
            max=self.max,
            value=self.value)


def all_gather(data):

    world_size = get_world_size()
    if world_size == 1:
        return [data]

    buffer = pickle.dumps(data)
    storage = torch.ByteStorage.from_buffer(buffer)
    tensor = torch.ByteTensor(storage).to("cuda")

    local_size = torch.tensor([tensor.numel()], device="cuda")
    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
    dist.all_gather(size_list, local_size)
    size_list = [int(size.item()) for size in size_list]
    max_size = max(size_list)

    tensor_list = []
    for _ in size_list:
        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
    if local_size != max_size:
        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
        tensor = torch.cat((tensor, padding), dim=0)
    dist.all_gather(tensor_list, tensor)

    data_list = []
    for size, tensor in zip(size_list, tensor_list):
        buffer = tensor.cpu().numpy().tobytes()[:size]
        data_list.append(pickle.loads(buffer))

    return data_list


def reduce_dict(input_dict, average=True):

    world_size = get_world_size()
    if world_size < 2:
        return input_dict
    with torch.no_grad():
        names = []
        values = []

        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


class MetricLogger(object):
    def __init__(self, delimiter="\t"):
        self.meters = defaultdict(SmoothedValue)
        self.delimiter = delimiter

    def update(self, **kwargs):
        for k, v in kwargs.items():
            if isinstance(v, torch.Tensor):
                v = v.item()
            assert isinstance(v, (float, int))
            self.meters[k].update(v)

    def __getattr__(self, attr):
        if attr in self.meters:
            return self.meters[attr]
        if attr in self.__dict__:
            return self.__dict__[attr]
        raise AttributeError("'{}' object has no attribute '{}'".format(
            type(self).__name__, attr))

    def __str__(self):
        loss_str = []
        for name, meter in self.meters.items():
            loss_str.append(
                "{}: {}".format(name, str(meter))
            )
        return self.delimiter.join(loss_str)

    def synchronize_between_processes(self):
        for meter in self.meters.values():
            meter.synchronize_between_processes()

    def add_meter(self, name, meter):
        self.meters[name] = meter

    def log_every(self, iterable, print_freq, header=None):
        i = 0
        if not header:
            header = ''
        start_time = time.time()
        end = time.time()
        iter_time = SmoothedValue(fmt='{avg:.4f}')
        data_time = SmoothedValue(fmt='{avg:.4f}')
        space_fmt = ':' + str(len(str(len(iterable)))) + 'd'
        if torch.cuda.is_available():
            log_msg = self.delimiter.join([
                header,
                '[{0' + space_fmt + '}/{1}]',
                'eta: {eta}',
                '{meters}',
                'time: {time}',
                'data: {data}',
                'max mem: {memory:.0f}'
            ])
        else:
            log_msg = self.delimiter.join([
                header,
                '[{0' + space_fmt + '}/{1}]',
                'eta: {eta}',
                '{meters}',
                'time: {time}',
                'data: {data}'
            ])
        MB = 1024.0 * 1024.0
        for obj in iterable:
            data_time.update(time.time() - end)
            yield obj
            iter_time.update(time.time() - end)
            if i % print_freq == 0 or i == len(iterable) - 1:
                eta_seconds = iter_time.global_avg * (len(iterable) - i)
                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
                if torch.cuda.is_available():
                    print(log_msg.format(
                        i, len(iterable), eta=eta_string,
                        meters=str(self),
                        time=str(iter_time), data=str(data_time),
                        memory=torch.cuda.max_memory_allocated() / MB))
                else:
                    print(log_msg.format(
                        i, len(iterable), eta=eta_string,
                        meters=str(self),
                        time=str(iter_time), data=str(data_time)))
            i += 1
            end = time.time()
        total_time = time.time() - start_time
        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
        print('{} Total time: {} ({:.4f} s / it)'.format(
            header, total_time_str, total_time / len(iterable)))


def get_sha():

    cwd = os.path.dirname(os.path.abspath(__file__))

    def _run(command):
        return subprocess.check_output(command, cwd=cwd).decode('ascii').strip()
    sha = 'N/A'
    diff = "clean"
    branch = 'N/A'
    try:
        sha = _run(['git', 'rev-parse', 'HEAD'])
        subprocess.check_output(['git', 'diff'], cwd=cwd)
        diff = _run(['git', 'diff-index', 'HEAD'])
        diff = "has uncommited changes" if diff else "clean"
        branch = _run(['git', 'rev-parse', '--abbrev-ref', 'HEAD'])
    except Exception:
        pass
    message = f"sha: {sha}, status: {diff}, branch: {branch}"
    return message


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


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]):

    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)

        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 _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:

    max_size = []
    for i in range(tensor_list[0].dim()):
        max_size_i = torch.max(torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)).to(torch.int64)
        max_size.append(max_size_i)
    max_size = tuple(max_size)

    padded_imgs = []
    padded_masks = []
    for img in tensor_list:
        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
        padded_imgs.append(padded_img)

        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
        padded_masks.append(padded_mask.to(torch.bool))

    tensor = torch.stack(padded_imgs)
    mask = torch.stack(padded_masks)

    return NestedTensor(tensor, mask=mask)


def setup_for_distributed(is_master):

    import builtins as __builtin__
    builtin_print = __builtin__.print

    def print(*args, **kwargs):
        force = kwargs.pop('force', False)
        if is_master or force:
            builtin_print(*args, **kwargs)

    __builtin__.print = print


def is_dist_avail_and_initialized():

    if not dist.is_available():
        return False
    if not dist.is_initialized():
        return False
    return True


def get_world_size():

    if not is_dist_avail_and_initialized():
        return 1
    return dist.get_world_size()


def get_rank():

    if not is_dist_avail_and_initialized():
        return 0
    return dist.get_rank()


def is_main_process():

    return get_rank() == 0


def save_on_master(*args, **kwargs):

    if is_main_process():
        torch.save(*args, **kwargs)


def init_distributed_mode(args):

    if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
        args.rank = int(os.environ["RANK"])
        args.world_size = int(os.environ['WORLD_SIZE'])
        args.gpu = int(os.environ['LOCAL_RANK'])
    elif 'SLURM_PROCID' in os.environ:
        args.rank = int(os.environ['SLURM_PROCID'])
        args.gpu = args.rank % torch.cuda.device_count()
    else:
        print('Not using distributed mode')
        args.distributed = False
        return

    args.distributed = True

    torch.cuda.set_device(args.gpu)
    args.dist_backend = 'nccl'
    print('| distributed init (rank {}): {}'.format(
        args.rank, args.dist_url), flush=True)
    torch.distributed.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
                                         world_size=args.world_size, rank=args.rank)
    torch.distributed.barrier()
    setup_for_distributed(args.rank == 0)


def accuracy(output, target, topk=(1,)):

    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):

    if version.parse(torchvision.__version__) < version.parse('0.7'):
        if input.numel() > 0:
            return torch.nn.functional.interpolate(
                input, size, scale_factor, mode, align_corners
            )

        output_shape = _output_size(2, input, size, scale_factor)
        output_shape = list(input.shape[:-2]) + list(output_shape)
        return _new_empty_tensor(input, output_shape)
    else:
        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)


def box_cxcywh_to_xyxy(x):
    x_c, y_c, w, h = x.unbind(-1)
    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
         (x_c + 0.5 * w), (y_c + 0.5 * h)]
    return torch.stack(b, dim=-1)


def box_xyxy_to_cxcywh(x):
    x0, y0, x1, y1 = x.unbind(-1)
    b = [(x0 + x1) / 2, (y0 + y1) / 2,
         (x1 - x0), (y1 - y0)]
    return torch.stack(b, dim=-1)


def box_iou(boxes1, boxes2):
    area1 = box_area(boxes1)
    area2 = box_area(boxes2)

    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])
    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])

    wh = (rb - lt).clamp(min=0)
    inter = wh[:, :, 0] * wh[:, :, 1]

    union = area1[:, None] + area2 - inter

    iou = inter / union
    return iou, union


def generalized_box_iou(boxes1, boxes2):

    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
    iou, union = box_iou(boxes1, boxes2)

    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])

    wh = (rb - lt).clamp(min=0)
    area = wh[:, :, 0] * wh[:, :, 1]

    return iou - (area - union) / area


def masks_to_boxes(masks):

    if masks.numel() == 0:
        return torch.zeros((0, 4), device=masks.device)

    h, w = masks.shape[-2:]

    y = torch.arange(0, h, dtype=torch.float)
    x = torch.arange(0, w, dtype=torch.float)
    y, x = torch.meshgrid(y, x)

    x_mask = (masks * x.unsqueeze(0))
    x_max = x_mask.flatten(1).max(-1)[0]
    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]

    y_mask = (masks * y.unsqueeze(0))
    y_max = y_mask.flatten(1).max(-1)[0]
    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]

    return torch.stack([x_min, y_min, x_max, y_max], 1)