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mmpretrain.models.selfsup.simclr 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Dict, List, Tuple

import torch
from mmengine.dist import all_gather, get_rank

from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from .base import BaseSelfSupervisor


class GatherLayer(torch.autograd.Function):
    """Gather tensors from all process, supporting backward propagation."""

    @staticmethod
    def forward(ctx: Any, input: torch.Tensor) -> Tuple[List]:
        ctx.save_for_backward(input)
        output = all_gather(input)
        return tuple(output)

    @staticmethod
    def backward(ctx: Any, *grads: torch.Tensor) -> torch.Tensor:
        input, = ctx.saved_tensors
        grad_out = torch.zeros_like(input)
        grad_out[:] = grads[get_rank()]
        return grad_out


[文档]@MODELS.register_module() class SimCLR(BaseSelfSupervisor): """SimCLR. Implementation of `A Simple Framework for Contrastive Learning of Visual Representations <https://arxiv.org/abs/2002.05709>`_. """ @staticmethod def _create_buffer( batch_size: int, device: torch.device ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: """Compute the mask and the index of positive samples. Args: batch_size (int): The batch size. device (torch.device): The device of backend. Returns: Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - The mask for feature selection. - The index of positive samples. - The mask of negative samples. """ mask = 1 - torch.eye(batch_size * 2, dtype=torch.uint8).to(device) pos_idx = ( torch.arange(batch_size * 2).to(device), 2 * torch.arange(batch_size, dtype=torch.long).unsqueeze(1).repeat( 1, 2).view(-1, 1).squeeze().to(device)) neg_mask = torch.ones((batch_size * 2, batch_size * 2 - 1), dtype=torch.uint8).to(device) neg_mask[pos_idx] = 0 return mask, pos_idx, neg_mask
[文档] def loss(self, inputs: List[torch.Tensor], data_samples: List[DataSample], **kwargs) -> Dict[str, torch.Tensor]: """The forward function in training. Args: inputs (List[torch.Tensor]): The input images. data_samples (List[DataSample]): All elements required during the forward function. Returns: Dict[str, torch.Tensor]: A dictionary of loss components. """ assert isinstance(inputs, list) inputs = torch.stack(inputs, 1) inputs = inputs.reshape((inputs.size(0) * 2, inputs.size(2), inputs.size(3), inputs.size(4))) x = self.backbone(inputs) z = self.neck(x)[0] # (2n)xd z = z / (torch.norm(z, p=2, dim=1, keepdim=True) + 1e-10) z = torch.cat(GatherLayer.apply(z), dim=0) # (2N)xd assert z.size(0) % 2 == 0 N = z.size(0) // 2 s = torch.matmul(z, z.permute(1, 0)) # (2N)x(2N) mask, pos_idx, neg_mask = self._create_buffer(N, s.device) # remove diagonal, (2N)x(2N-1) s = torch.masked_select(s, mask == 1).reshape(s.size(0), -1) positive = s[pos_idx].unsqueeze(1) # (2N)x1 # select negative, (2N)x(2N-2) negative = torch.masked_select(s, neg_mask == 1).reshape(s.size(0), -1) loss = self.head.loss(positive, negative) losses = dict(loss=loss) return losses
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