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.rotation_pred

# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner import auto_fp16

from ..builder import ALGORITHMS, build_backbone, build_head
from .base import BaseModel


@ALGORITHMS.register_module()
class RotationPred(BaseModel):
    """Rotation prediction.

    Implementation of `Unsupervised Representation Learning
    by Predicting Image Rotations <https://arxiv.org/abs/1803.07728>`_.

    Args:
        backbone (dict): Config dict for module of backbone.
        head (dict): Config dict for module of loss functions.
            Defaults to None.
    """

    def __init__(self, backbone, head=None, init_cfg=None):
        super(RotationPred, self).__init__(init_cfg)
        self.backbone = build_backbone(backbone)
        assert head is not None
        self.head = build_head(head)

    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, rot_label, **kwargs):
        """Forward computation during training.

        Args:
            img (Tensor): Input images of shape (N, C, H, W).
                Typically these should be mean centered and std scaled.
            rot_label (Tensor): Labels for the rotations.
            kwargs: Any keyword arguments to be used to forward.

        Returns:
            dict[str, Tensor]: A dictionary of loss components.
        """
        x = self.extract_feat(img)
        outs = self.head(x)
        loss_inputs = (outs, rot_label)
        losses = self.head.loss(*loss_inputs)
        return losses

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

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

        Returns:
            dict[str, Tensor]: A dictionary of output features.
        """
        x = self.extract_feat(img)  # tuple
        outs = self.head(x)
        keys = [f'head{i}' for i in self.backbone.out_indices]
        out_tensors = [out.cpu() for out in outs]  # NxC
        return dict(zip(keys, out_tensors))

    @auto_fp16(apply_to=('img', ))
    def forward(self, img, rot_label=None, mode='train', **kwargs):
        """Forward function to select mode and modify the input image shape.

        Args:
            img (Tensor): Input images, the shape depends on mode.
                Typically these should be mean centered and std scaled.
        """
        if mode != 'extract' and img.dim() == 5:  # Nx4xCxHxW
            assert rot_label.dim() == 2  # Nx4
            img = img.view(
                img.size(0) * img.size(1), img.size(2), img.size(3),
                img.size(4))  # (4N)xCxHxW
            rot_label = torch.flatten(rot_label)  # (4N)
        if mode == 'train':
            return self.forward_train(img, rot_label, **kwargs)
        elif mode == 'test':
            return self.forward_test(img, **kwargs)
        elif mode == 'extract':
            return self.extract_feat(img)
        else:
            raise Exception(f'No such mode: {mode}')