Pikaliov
1b3206b6a7
- Add enhanced README with project structure and quick start guide - Initialize repository with DCNv4 CUDA extension (PyTorch module) - Include classification, detection, and segmentation subdirectories - Reference upstream OpenGVLab DCNv4 implementation Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
# --------------------------------------------------------
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# DCNv4
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# Copyright (c) 2024 OpenGVLab
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# Licensed under The MIT License [see LICENSE for details]
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# --------------------------------------------------------
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import torch
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import os
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import math
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from torch.utils.data.sampler import Sampler
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import torch.distributed as dist
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import numpy as np
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class SubsetRandomSampler(torch.utils.data.Sampler):
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"""Samples elements randomly from a given list of indices, without replacement.
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Arguments:
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indices (sequence): a sequence of indices
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"""
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def __init__(self, indices):
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self.epoch = 0
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self.indices = indices
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def __iter__(self):
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return (self.indices[i] for i in torch.randperm(len(self.indices)))
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def __len__(self):
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return len(self.indices)
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def set_epoch(self, epoch):
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self.epoch = epoch
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class NodeDistributedSampler(Sampler):
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"""Sampler that restricts data loading to a subset of the dataset.
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It is especially useful in conjunction with
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:class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
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process can pass a DistributedSampler instance as a DataLoader sampler,
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and load a subset of the original dataset that is exclusive to it.
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.. note::
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Dataset is assumed to be of constant size.
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Arguments:
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dataset: Dataset used for sampling.
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num_replicas (optional): Number of processes participating in
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distributed training.
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rank (optional): Rank of the current process within num_replicas.
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"""
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def __init__(self,
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dataset,
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num_replicas=None,
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rank=None,
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local_rank=None,
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local_size=None):
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if num_replicas is None:
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if not dist.is_available():
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raise RuntimeError(
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"Requires distributed package to be available")
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num_replicas = dist.get_world_size()
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if rank is None:
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if not dist.is_available():
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raise RuntimeError(
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"Requires distributed package to be available")
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rank = dist.get_rank()
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if local_rank is None:
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local_rank = int(os.environ.get('LOCAL_RANK', 0))
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if local_size is None:
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local_size = int(os.environ.get('LOCAL_SIZE', 1))
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self.dataset = dataset
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self.num_replicas = num_replicas
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self.num_parts = local_size
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self.rank = rank
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self.local_rank = local_rank
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self.epoch = 0
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self.num_samples = int(
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math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
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self.total_size = self.num_samples * self.num_replicas
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self.total_size_parts = self.num_samples * self.num_replicas // self.num_parts
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def __iter__(self):
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# deterministically shuffle based on epoch
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g = torch.Generator()
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g.manual_seed(self.epoch)
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t = torch.Generator()
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t.manual_seed(0)
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indices = torch.randperm(len(self.dataset), generator=t).tolist()
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# indices = range(len(self.dataset))
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indices = [i for i in indices if i % self.num_parts == self.local_rank]
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# add extra samples to make it evenly divisible
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indices += indices[:(self.total_size_parts - len(indices))]
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assert len(indices) == self.total_size_parts
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# subsample
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indices = indices[self.rank // self.num_parts:self.
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total_size_parts:self.num_replicas // self.num_parts]
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index = torch.randperm(len(indices), generator=g).tolist()
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indices = list(np.array(indices)[index])
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assert len(indices) == self.num_samples
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return iter(indices)
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def __len__(self):
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return self.num_samples
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def set_epoch(self, epoch):
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self.epoch = epoch
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