util/misc.py

"""Misc functions, including distributed helpers.

Mostly copy-paste from torchvision references.
"""
from mmcv.runner import get_dist_info, init_dist
import os
import random
import subprocess
import time
from collections import OrderedDict, defaultdict, deque
import datetime
import pickle
from typing import Optional, List

import socket
import json, time
import numpy as np
import torch
import torch.distributed as dist
from torch import Tensor
import logging

import colorsys

import torchvision
__torchvision_need_compat_flag = float(
    torchvision.__version__.split('.')[1]) < 7
if __torchvision_need_compat_flag:
    from torchvision.ops import _new_empty_tensor
    from torchvision.ops.misc import _output_size


def is_free_port(port: int) -> bool:
    ips = socket.gethostbyname_ex(socket.gethostname())[-1]
    ips.append('localhost')
    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
        return all(s.connect_ex((ip, port)) != 0 for ip in ips)


def find_free_port() -> str:
    # Copied from https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/launch.py # noqa: E501
    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    # Binding to port 0 will cause the OS to find an available port for us
    sock.bind(('', 0))
    port = sock.getsockname()[1]
    sock.close()
    # NOTE: there is still a chance the port could be taken by other processes.
    return port


class SmoothedValue(object):
    """Track a series of values and provide access to smoothed values over a
    window or the global series average."""
    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):
        """
        Warning: does not synchronize the deque!
        """
        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]

    @property
    def median(self):
        d = torch.tensor(list(self.deque))
        if d.shape[0] == 0:
            return 0
        return d.median().item()

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

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

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

    @property
    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):
    """
    Run all_gather on arbitrary picklable data (not necessarily tensors)
    Args:
        data: any picklable object
    Returns:
        list[data]: list of data gathered from each rank
    """
    world_size = get_world_size()
    if world_size == 1:
        return [data]

    # serialized to a Tensor
    buffer = pickle.dumps(data)
    storage = torch.ByteStorage.from_buffer(buffer)
    tensor = torch.ByteTensor(storage).to('cuda')

    # obtain Tensor size of each rank
    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):
    """
    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 = 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
        # import pdb; pdb.set_trace()
        for k in sorted(input_dict.keys()):

            names.append(k)
            values.append(input_dict[k])
        # pdb.set_trace()
        values = torch.stack(values, dim=0)
        
        try:
            dist.all_reduce(values)
            rank = dist.get_rank()
            # logging.info(f'Rank {rank} after all_reduce')
        except Exception as e:
            rank = dist.get_rank()
            print(f'Exception in rank {rank}: {e}')
            # print(f'values: {values}')
            # print(f'names: {names}')
            logging.info(f'Rank {rank} after all_reduce')
        if average:
            values /= world_size
        reduced_dict = {k: v for k, v in zip(names, values)}
    return reduced_dict

def setup_logging():
    logging.basicConfig(level=logging.INFO)
    rank = dist.get_rank()
    logging.info(f'Rank {rank} before all_reduce')

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():
            if meter.count > 0:
                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, logger=None):
        if logger is None:
            print_func = print
        else:
            print_func = logger.info

        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
            # import pdb; pdb.set_trace()
            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_func(
                        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_func(
                        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_func('{} Total time: {} ({:.4f} s / it)'.format(
            header, total_time_str, total_time / len(iterable)))


class LogBuffer:
    def __init__(self):
        self.val_history = OrderedDict()
        self.n_history = OrderedDict()
        self.output = OrderedDict()
        self.ready = False

    def clear(self) -> None:
        self.val_history.clear()
        self.n_history.clear()
        self.clear_output()

    def clear_output(self) -> None:
        self.output.clear()
        self.ready = False

    def update(self, vars: dict, count: int = 1) -> None:
        assert isinstance(vars, dict)
        for key, var in vars.items():
            if key not in self.val_history:
                self.val_history[key] = []
                self.n_history[key] = []
            self.val_history[key].append(var)
            self.n_history[key].append(count)

    def average(self, n: int = 0) -> None:
        """Average latest n values or all values."""
        assert n >= 0
        for key in self.val_history:
            values = np.array(self.val_history[key][-n:])
            nums = np.array(self.n_history[key][-n:])
            avg = np.sum(values * nums) / np.sum(nums)
            self.output[key] = avg
        self.ready = True


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):
    # import pdb; pdb.set_trace()
    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
        if mask == 'auto':
            self.mask = torch.zeros_like(tensors).to(tensors.device)
            if self.mask.dim() == 3:
                self.mask = self.mask.sum(0).to(bool)
            elif self.mask.dim() == 4:
                self.mask = self.mask.sum(1).to(bool)
            else:
                raise ValueError(
                    'tensors dim must be 3 or 4 but {}({})'.format(
                        self.tensors.dim(), self.tensors.shape))

    def imgsize(self):
        res = []
        for i in range(self.tensors.shape[0]):
            mask = self.mask[i]
            maxH = (~mask).sum(0).max()
            maxW = (~mask).sum(1).max()
            res.append(torch.Tensor([maxH, maxW]))
        return res

    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 to_img_list_single(self, tensor, mask):
        assert tensor.dim() == 3, 'dim of tensor should be 3 but {}'.format(
            tensor.dim())
        maxH = (~mask).sum(0).max()
        maxW = (~mask).sum(1).max()
        img = tensor[:, :maxH, :maxW]
        return img

    def to_img_list(self):
        """remove the padding and convert to img list
        Returns:
            [type]: [description]
        """
        if self.tensors.dim() == 3:
            return self.to_img_list_single(self.tensors, self.mask)
        else:
            res = []
            for i in range(self.tensors.shape[0]):
                tensor_i = self.tensors[i]
                mask_i = self.mask[i]
                res.append(self.to_img_list_single(tensor_i, mask_i))
            return res

    @property
    def device(self):
        return self.tensors.device

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

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

    @property
    def shape(self):
        return {
            'tensors.shape': self.tensors.shape,
            'mask.shape': self.mask.shape
        }


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)


@torch.jit.unused
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):
    """This function disables printing when not in master process."""
    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 'WORLD_SIZE' in os.environ and os.environ['WORLD_SIZE'] != '':
        local_world_size = int(os.environ['WORLD_SIZE'])
        args.world_size = args.world_size * local_world_size
        args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
        args.rank = args.rank * local_world_size + args.local_rank
        print('world size: {}, rank: {}, local rank: {}'.format(args.world_size, args.rank, args.local_rank))
        print(json.dumps(dict(os.environ), indent=2))
    elif 'SLURM_PROCID' in os.environ:
        args.rank = int(os.environ['SLURM_PROCID'])
        args.gpu = args.local_rank = int(os.environ['SLURM_LOCALID'])
        args.world_size = int(os.environ['SLURM_NPROCS'])
        
        print('world size: {}, world rank: {}, local rank: {}, device_count: {}'.format(args.world_size, args.rank, args.local_rank, torch.cuda.device_count()))
        print("os.environ['SLURM_JOB_NODELIST']:", os.environ['SLURM_JOB_NODELIST'])
        print(json.dumps(dict(os.environ), indent=2))
        print('args:')
        print(json.dumps(vars(args), indent=2))
    else:
        print('Not using distributed mode')
        args.distributed = False
        args.world_size = 1
        args.rank = 0
        args.local_rank = 0
        return

    print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
    args.distributed = True
    torch.cuda.set_device(args.local_rank)
    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)
    print("Before torch.distributed.barrier()")
    torch.distributed.barrier()
    print("End torch.distributed.barrier()")
    setup_for_distributed(args.rank == 0)


@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.
    """
    if __torchvision_need_compat_flag < 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)


class color_sys():
    def __init__(self, num_colors) -> None:
        self.num_colors = num_colors
        colors = []
        for i in np.arange(0., 360., 360. / num_colors):
            hue = i / 360.
            lightness = (50 + np.random.rand() * 10) / 100.
            saturation = (90 + np.random.rand() * 10) / 100.
            colors.append(
                tuple([
                    int(j * 255)
                    for j in colorsys.hls_to_rgb(hue, lightness, saturation)
                ]))
        self.colors = colors

    def __call__(self, idx):
        return self.colors[idx]


def inverse_sigmoid(x, eps=1e-3):
    x = x.clamp(min=0, max=1)
    x1 = x.clamp(min=eps)
    x2 = (1 - x).clamp(min=eps)
    return torch.log(x1 / x2)


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