init

.gitignore

+.idea
+.DS_Store
\ No newline at end of file

trainer/trainer.py

+import torch
+
+from utils.utils import *
+
+
+def train_one_epoch(model, optimizer, data_loader, device, epoch, loss, loss_weights, warmup=False):
+    lr_scheduler = None
+    log_bar = None
+    if is_main_process():
+        log_bar = ProgBar(len(data_loader))
+    for i, (inputs, targets) in enumerate(data_loader):
+        if isinstance(inputs, torch.Tensor):
+            inputs = [inputs]
+        if isinstance(targets, torch.Tensor):
+            targets = [targets]
+        for idx_inputs in range(len(inputs)):
+            inputs[idx_inputs] = inputs[idx_inputs].to(device)
+        for idx_target in range(len(targets)):
+            targets[idx_target] = targets[idx_target].to(device)
+        outputs = model(*inputs)
+        if isinstance(outputs, torch.Tensor):
+            outputs = [outputs]
+
+        losses = []
+        it = zip(outputs, targets, loss, loss_weights)
+        for output, target, loss_dict, loss_weight in it:
+            loss_value_dict = dict()
+            for loss_name, loss_fn in loss_dict.items():
+                loss_value = loss_fn(output, target)
+                loss_value_dict.update({loss_name: loss_value * loss_weight})
+            losses.append(loss_value_dict)
+        losses_value = sum([sum(loss_v for loss_v in loss_value_dict.values()) for loss_value_dict in losses])
+        optimizer.zero_grad()
+        losses_value.backward()
+        optimizer.step()
+        loss_dict_reduced = []
+        for loss_value_dict in losses:
+            loss_dict_reduced.append(reduce_dict(loss_value_dict))
+        if is_main_process() and log_bar is not None:
+            logs = []
+            for l_i, loss_dict in enumerate(loss_dict_reduced):
+                for k, v in loss_dict.items():
+                    logs.append((k + "_%d" % l_i, v.item()))
+            log_bar.update(i + 1, logs)
+    return None
\ No newline at end of file

utils/utils.py

+import sys
+from collections import defaultdict, deque
+import datetime
+import pickle
+import time
+
+import torch
+import torch.distributed as dist
+
+import errno
+import os
+import random
+
+import numpy as np
+
+
+class ProgBar(object):
+    """Displays a progress bar.
+
+    Arguments:
+        target: Total number of steps expected, None if unknown.
+        width: Progress bar width on screen.
+        verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
+        stateful_metrics: Iterable of string names of metrics that should *not* be
+          averaged over time. Metrics in this list will be displayed as-is. All
+          others will be averaged by the progbar before display.
+        interval: Minimum visual progress update interval (in seconds).
+        unit_name: Display name for step counts (usually "step" or "sample").
+    """
+
+    def __init__(self,
+                 target,
+                 width=30,
+                 verbose=1,
+                 interval=0.05,
+                 stateful_metrics=None,
+                 unit_name='step'):
+        self.target = target
+        self.width = width
+        self.verbose = verbose
+        self.interval = interval
+        self.unit_name = unit_name
+        if stateful_metrics:
+            self.stateful_metrics = set(stateful_metrics)
+        else:
+            self.stateful_metrics = set()
+
+        self._dynamic_display = ((hasattr(sys.stdout, 'isatty') and
+                                  sys.stdout.isatty()) or
+                                 'ipykernel' in sys.modules or
+                                 'posix' in sys.modules or
+                                 'PYCHARM_HOSTED' in os.environ)
+        self._total_width = 0
+        self._seen_so_far = 0
+        # We use a dict + list to avoid garbage collection
+        # issues found in OrderedDict
+        self._values = {}
+        self._values_order = []
+        self._start = time.time()
+        self._last_update = 0
+
+        self._time_after_first_step = None
+
+    def update(self, current, values=None, finalize=None):
+        """Updates the progress bar.
+
+        Arguments:
+            current: Index of current step.
+            values: List of tuples: `(name, value_for_last_step)`. If `name` is in
+              `stateful_metrics`, `value_for_last_step` will be displayed as-is.
+              Else, an average of the metric over time will be displayed.
+            finalize: Whether this is the last update for the progress bar. If
+              `None`, defaults to `current >= self.target`.
+        """
+        if finalize is None:
+            if self.target is None:
+                finalize = False
+            else:
+                finalize = current >= self.target
+
+        values = values or []
+        for k, v in values:
+            if k not in self._values_order:
+                self._values_order.append(k)
+            if k not in self.stateful_metrics:
+                # In the case that progress bar doesn't have a target value in the first
+                # epoch, both on_batch_end and on_epoch_end will be called, which will
+                # cause 'current' and 'self._seen_so_far' to have the same value. Force
+                # the minimal value to 1 here, otherwise stateful_metric will be 0s.
+                value_base = max(current - self._seen_so_far, 1)
+                if k not in self._values:
+                    self._values[k] = [v * value_base, value_base]
+                else:
+                    self._values[k][0] += v * value_base
+                    self._values[k][1] += value_base
+            else:
+                # Stateful metrics output a numeric value. This representation
+                # means "take an average from a single value" but keeps the
+                # numeric formatting.
+                self._values[k] = [v, 1]
+        self._seen_so_far = current
+
+        now = time.time()
+        info = ' - %.0fs' % (now - self._start)
+        if self.verbose == 1:
+            if now - self._last_update < self.interval and not finalize:
+                return
+
+            prev_total_width = self._total_width
+            if self._dynamic_display:
+                sys.stdout.write('\b' * prev_total_width)
+                sys.stdout.write('\r')
+            else:
+                sys.stdout.write('\n')
+
+            if self.target is not None:
+                numdigits = int(np.log10(self.target)) + 1
+                bar = ('%' + str(numdigits) + 'd/%d [') % (current, self.target)
+                prog = float(current) / self.target
+                prog_width = int(self.width * prog)
+                if prog_width > 0:
+                    bar += ('=' * (prog_width - 1))
+                    if current < self.target:
+                        bar += '>'
+                    else:
+                        bar += '='
+                bar += ('.' * (self.width - prog_width))
+                bar += ']'
+            else:
+                bar = '%7d/Unknown' % current
+
+            self._total_width = len(bar)
+            sys.stdout.write(bar)
+
+            time_per_unit = self._estimate_step_duration(current, now)
+
+            if self.target is None or finalize:
+                if time_per_unit >= 1 or time_per_unit == 0:
+                    info += ' %.0fs/%s' % (time_per_unit, self.unit_name)
+                elif time_per_unit >= 1e-3:
+                    info += ' %.0fms/%s' % (time_per_unit * 1e3, self.unit_name)
+                else:
+                    info += ' %.0fus/%s' % (time_per_unit * 1e6, self.unit_name)
+            else:
+                eta = time_per_unit * (self.target - current)
+                if eta > 3600:
+                    eta_format = '%d:%02d:%02d' % (eta // 3600,
+                                                   (eta % 3600) // 60, eta % 60)
+                elif eta > 60:
+                    eta_format = '%d:%02d' % (eta // 60, eta % 60)
+                else:
+                    eta_format = '%ds' % eta
+
+                info = ' - ETA: %s' % eta_format
+
+            for k in self._values_order:
+                info += ' - %s:' % k
+                if isinstance(self._values[k], list):
+                    avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
+                    if abs(avg) > 1e-3:
+                        info += ' %.4f' % avg
+                    else:
+                        info += ' %.4e' % avg
+                else:
+                    info += ' %s' % self._values[k]
+
+            self._total_width += len(info)
+            if prev_total_width > self._total_width:
+                info += (' ' * (prev_total_width - self._total_width))
+
+            if finalize:
+                info += '\n'
+
+            sys.stdout.write(info)
+            sys.stdout.flush()
+
+        elif self.verbose == 2:
+            if finalize:
+                numdigits = int(np.log10(self.target)) + 1
+                count = ('%' + str(numdigits) + 'd/%d') % (current, self.target)
+                info = count + info
+                for k in self._values_order:
+                    info += ' - %s:' % k
+                    avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
+                    if avg > 1e-3:
+                        info += ' %.4f' % avg
+                    else:
+                        info += ' %.4e' % avg
+                info += '\n'
+
+                sys.stdout.write(info)
+                sys.stdout.flush()
+
+        self._last_update = now
+
+    def add(self, n, values=None):
+        self.update(self._seen_so_far + n, values)
+
+    def _estimate_step_duration(self, current, now):
+        """Estimate the duration of a single step.
+
+        Given the step number `current` and the corresponding time `now`
+        this function returns an estimate for how long a single step
+        takes. If this is called before one step has been completed
+        (i.e. `current == 0`) then zero is given as an estimate. The duration
+        estimate ignores the duration of the (assumed to be non-representative)
+        first step for estimates when more steps are available (i.e. `current>1`).
+        Arguments:
+          current: Index of current step.
+          now: The current time.
+        Returns: Estimate of the duration of a single step.
+        """
+        if current:
+            # there are a few special scenarios here:
+            # 1) somebody is calling the progress bar without ever supplying step 1
+            # 2) somebody is calling the progress bar and supplies step one mulitple
+            #    times, e.g. as part of a finalizing call
+            # in these cases, we just fall back to the simple calculation
+            if self._time_after_first_step is not None and current > 1:
+                time_per_unit = (now - self._time_after_first_step) / (current - 1)
+            else:
+                time_per_unit = (now - self._start) / current
+
+            if current == 1:
+                self._time_after_first_step = now
+            return time_per_unit
+        else:
+            return 0
+
+
+def default(method):
+    """Decorates a method to detect overrides in subclasses."""
+    method._is_default = True  # pylint: disable=protected-access
+    return method
+
+
+def is_default(method):
+    """Check if a method is decorated with the `default` wrapper."""
+    return getattr(method, '_is_default', False)
+
+
+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))
+        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 datasets (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of datasets 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)
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    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
+        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}',
+                'datasets: {datasets}',
+                'max mem: {memory:.0f}'
+            ])
+        else:
+            log_msg = self.delimiter.join([
+                header,
+                '[{0' + space_fmt + '}/{1}]',
+                'eta: {eta}',
+                '{meters}',
+                'time: {time}',
+                'datasets: {datasets}'
+            ])
+        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 collate_fn(batch):
+    # return tuple(batch[0]), batch[1], tuple(batch[2])
+    return tuple(zip(*batch))
+
+
+def warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor):
+    def f(x):
+        if x >= warmup_iters:
+            return 1
+        alpha = float(x) / warmup_iters
+        return warmup_factor * (1 - alpha) + alpha
+
+    return torch.optim.lr_scheduler.LambdaLR(optimizer, f)
+
+
+def mkdir(path):
+    try:
+        os.makedirs(path)
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+
+
+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 '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 setup(rank, world_size, addr="127.0.0.1", port="1895"):
+    os.environ['MASTER_ADDR'] = addr
+    os.environ['MASTER_PORT'] = port
+
+    dist.init_process_group("nccl", rank=rank, world_size=world_size)
+
+
+def cleanup():
+    dist.destroy_process_group()
+
+
+def select(prob_a):
+    num_ = ['choice_a', 'choice_b']
+    # 概率列表
+    r_ = [prob_a, 1 - prob_a]
+    sum_ = 0
+    ran = random.random()
+    for num, r in zip(num_, r_):
+        sum_ += r
+        if ran < sum_: break
+    return num
+
+
+import logging
+
+
+def get_logger(filename, verbosity=1, name=None):
+    level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}
+    formatter = logging.Formatter(
+        "[%(asctime)s][%(filename)s][line:%(lineno)d][%(levelname)s] %(message)s"
+    )
+    logger = logging.getLogger(name)
+    logger.setLevel(level_dict[verbosity])
+
+    fh = logging.FileHandler(filename, "w")
+    fh.setFormatter(formatter)
+    logger.addHandler(fh)
+
+    sh = logging.StreamHandler()
+    sh.setFormatter(formatter)
+    logger.addHandler(sh)
+
+    return logger