Note

You are reading the documentation for MMSelfSup 0.x, which will soon be deprecated by the end of 2022. We recommend you upgrade to MMSelfSup 1.0.0rc versions to enjoy fruitful new features and better performance brought by OpenMMLab 2.0. Check out the changelog, code and documentation of MMSelfSup 1.0.0rc for more details.

Source code for mmselfsup.models.algorithms.moco

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn

from mmselfsup.utils import (batch_shuffle_ddp, batch_unshuffle_ddp,
                             concat_all_gather)
from ..builder import ALGORITHMS, build_backbone, build_head, build_neck
from .base import BaseModel


@ALGORITHMS.register_module()
class MoCo(BaseModel):
    """MoCo.

    Implementation of `Momentum Contrast for Unsupervised Visual
    Representation Learning <https://arxiv.org/abs/1911.05722>`_.
    Part of the code is borrowed from:
    `<https://github.com/facebookresearch/moco/blob/master/moco/builder.py>`_.

    Args:
        backbone (dict): Config dict for module of backbone.
        neck (dict): Config dict for module of deep features to compact
            feature vectors. Defaults to None.
        head (dict): Config dict for module of loss functions.
            Defaults to None.
        queue_len (int): Number of negative keys maintained in the queue.
            Defaults to 65536.
        feat_dim (int): Dimension of compact feature vectors. Defaults to 128.
        momentum (float): Momentum coefficient for the momentum-updated
            encoder. Defaults to 0.999.
    """

    def __init__(self,
                 backbone,
                 neck=None,
                 head=None,
                 queue_len=65536,
                 feat_dim=128,
                 momentum=0.999,
                 init_cfg=None,
                 **kwargs):
        super(MoCo, self).__init__(init_cfg)
        assert neck is not None
        self.encoder_q = nn.Sequential(
            build_backbone(backbone), build_neck(neck))
        self.encoder_k = nn.Sequential(
            build_backbone(backbone), build_neck(neck))

        for param_q, param_k in zip(self.encoder_q.parameters(),
                                    self.encoder_k.parameters()):
            param_k.data.copy_(param_q.data)
            param_k.requires_grad = False

        self.backbone = self.encoder_q[0]
        self.neck = self.encoder_q[1]
        assert head is not None
        self.head = build_head(head)

        self.queue_len = queue_len
        self.momentum = momentum

        # create the queue
        self.register_buffer('queue', torch.randn(feat_dim, queue_len))
        self.queue = nn.functional.normalize(self.queue, dim=0)
        self.register_buffer('queue_ptr', torch.zeros(1, dtype=torch.long))

    @torch.no_grad()
    def _momentum_update_key_encoder(self):
        """Momentum update of the key encoder."""
        for param_q, param_k in zip(self.encoder_q.parameters(),
                                    self.encoder_k.parameters()):
            param_k.data = param_k.data * self.momentum + \
                           param_q.data * (1. - self.momentum)

    @torch.no_grad()
    def _dequeue_and_enqueue(self, keys):
        """Update queue."""
        # gather keys before updating queue
        keys = concat_all_gather(keys)

        batch_size = keys.shape[0]

        ptr = int(self.queue_ptr)
        assert self.queue_len % batch_size == 0  # for simplicity

        # replace the keys at ptr (dequeue and enqueue)
        self.queue[:, ptr:ptr + batch_size] = keys.transpose(0, 1)
        ptr = (ptr + batch_size) % self.queue_len  # move pointer

        self.queue_ptr[0] = ptr

    def extract_feat(self, img):
        """Function to extract features from backbone.

        Args:
            img (Tensor): Input images of shape (N, C, H, W).
                Typically these should be mean centered and std scaled.

        Returns:
            tuple[Tensor]: backbone outputs.
        """
        x = self.backbone(img)
        return x

    def forward_train(self, img, **kwargs):
        """Forward computation during training.

        Args:
            img (list[Tensor]): A list of input images with shape
                (N, C, H, W). Typically these should be mean centered
                and std scaled.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        assert isinstance(img, list)
        im_q = img[0]
        im_k = img[1]
        # compute query features
        q = self.encoder_q(im_q)[0]  # queries: NxC
        q = nn.functional.normalize(q, dim=1)

        # compute key features
        with torch.no_grad():  # no gradient to keys
            # update the key encoder
            self._momentum_update_key_encoder()

            # shuffle for making use of BN
            im_k, idx_unshuffle = batch_shuffle_ddp(im_k)

            k = self.encoder_k(im_k)[0]  # keys: NxC
            k = nn.functional.normalize(k, dim=1)

            # undo shuffle
            k = batch_unshuffle_ddp(k, idx_unshuffle)

        # compute logits
        # Einstein sum is more intuitive
        # positive logits: Nx1
        l_pos = torch.einsum('nc,nc->n', [q, k]).unsqueeze(-1)
        # negative logits: NxK
        l_neg = torch.einsum('nc,ck->nk', [q, self.queue.clone().detach()])

        losses = self.head(l_pos, l_neg)

        # update the queue
        self._dequeue_and_enqueue(k)

        return losses