Source code for mmagic.models.editors.tof.tof_vsr_net

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule

from mmagic.models.utils import flow_warp
from mmagic.registry import MODELS


@MODELS.register_module()
class TOFlowVSRNet(BaseModule):
    """PyTorch implementation of TOFlow.

    In TOFlow, the LR frames are pre-upsampled and have the same size with
    the GT frames.

    Paper: Xue et al., Video Enhancement with Task-Oriented Flow, IJCV 2018
    Code reference:

    1. https://github.com/anchen1011/toflow
    2. https://github.com/Coldog2333/pytoflow

    Args:
        adapt_official_weights (bool): Whether to adapt the weights translated
            from the official implementation. Set to false if you want to
            train from scratch. Default: False
    """

    def __init__(self, adapt_official_weights=False, init_cfg=None):
        super().__init__(init_cfg=init_cfg)

        self.adapt_official_weights = adapt_official_weights
        self.ref_idx = 0 if adapt_official_weights else 3

        # flow estimation module
        self.spynet = SPyNet()

        # reconstruction module
        self.conv1 = nn.Conv2d(3 * 7, 64, 9, 1, 4)
        self.conv2 = nn.Conv2d(64, 64, 9, 1, 4)
        self.conv3 = nn.Conv2d(64, 64, 1)
        self.conv4 = nn.Conv2d(64, 3, 1)

        # activation function
        self.relu = nn.ReLU(inplace=True)

    def forward(self, lrs):
        """
        Args:
            lrs: Input lr frames: (b, 7, 3, h, w).

        Returns:
            Tensor: SR frame: (b, 3, h, w).
        """
        # In the official implementation, the 0-th frame is the reference frame
        if self.adapt_official_weights:
            lrs = lrs[:, [3, 0, 1, 2, 4, 5, 6], :, :, :]

        num_batches, _, _, h, w = lrs.size()

        lr_ref = lrs[:, self.ref_idx, :, :, :]
        lr_aligned = []
        for i in range(7):  # 7 frames
            if i == self.ref_idx:
                lr_aligned.append(lr_ref)
            else:
                lr_supp = lrs[:, i, :, :, :]
                flow = self.spynet(lr_ref, lr_supp)
                lr_aligned.append(flow_warp(lr_supp, flow.permute(0, 2, 3, 1)))

        # reconstruction
        hr = torch.stack(lr_aligned, dim=1)
        hr = hr.view(num_batches, -1, h, w)
        hr = self.relu(self.conv1(hr))
        hr = self.relu(self.conv2(hr))
        hr = self.relu(self.conv3(hr))
        hr = self.conv4(hr) + lr_ref

        return hr


class BasicModule(nn.Module):
    """Basic module of SPyNet.

    Note that unlike the common spynet architecture, the basic module here
    contains batch normalization.
    """

    def __init__(self):
        super().__init__()

        self.basic_module = nn.Sequential(
            ConvModule(
                in_channels=8,
                out_channels=32,
                kernel_size=7,
                stride=1,
                padding=3,
                norm_cfg=dict(type='BN'),
                act_cfg=dict(type='ReLU')),
            ConvModule(
                in_channels=32,
                out_channels=64,
                kernel_size=7,
                stride=1,
                padding=3,
                norm_cfg=dict(type='BN'),
                act_cfg=dict(type='ReLU')),
            ConvModule(
                in_channels=64,
                out_channels=32,
                kernel_size=7,
                stride=1,
                padding=3,
                norm_cfg=dict(type='BN'),
                act_cfg=dict(type='ReLU')),
            ConvModule(
                in_channels=32,
                out_channels=16,
                kernel_size=7,
                stride=1,
                padding=3,
                norm_cfg=dict(type='BN'),
                act_cfg=dict(type='ReLU')),
            ConvModule(
                in_channels=16,
                out_channels=2,
                kernel_size=7,
                stride=1,
                padding=3,
                norm_cfg=None,
                act_cfg=None))

    def forward(self, tensor_input):
        """
        Args:
            tensor_input (Tensor): Input tensor with shape (b, 8, h, w).
                8 channels contain:
                [reference image (3), neighbor image (3), initial flow (2)].

        Returns:
            Tensor: Estimated flow with shape (b, 2, h, w)
        """
        return self.basic_module(tensor_input)


class SPyNet(nn.Module):
    """SPyNet architecture.

    Note that this implementation is specifically for TOFlow. It differs from
    the common SPyNet in the following aspects:
        1. The basic modules here contain BatchNorm.
        2. Normalization and denormalization are not done here, as
            they are done in TOFlow.
    Paper:
        Optical Flow Estimation using a Spatial Pyramid Network
    Code reference:
        https://github.com/Coldog2333/pytoflow
    """

    def __init__(self):
        super().__init__()

        self.basic_module = nn.ModuleList([BasicModule() for _ in range(4)])

    def forward(self, ref, supp):
        """
        Args:
            ref (Tensor): Reference image with shape of (b, 3, h, w).
            supp: The supporting image to be warped: (b, 3, h, w).

        Returns:
            Tensor: Estimated optical flow: (b, 2, h, w).
        """
        num_batches, _, h, w = ref.size()
        ref = [ref]
        supp = [supp]

        # generate downsampled frames
        for _ in range(3):
            ref.insert(
                0,
                F.avg_pool2d(
                    input=ref[0],
                    kernel_size=2,
                    stride=2,
                    count_include_pad=False))
            supp.insert(
                0,
                F.avg_pool2d(
                    input=supp[0],
                    kernel_size=2,
                    stride=2,
                    count_include_pad=False))

        # flow computation
        flow = ref[0].new_zeros(num_batches, 2, h // 16, w // 16)
        for i in range(4):
            flow_up = F.interpolate(
                input=flow,
                scale_factor=2,
                mode='bilinear',
                align_corners=True) * 2.0
            flow = flow_up + self.basic_module[i](
                torch.cat([
                    ref[i],
                    flow_warp(supp[i], flow_up.permute(0, 2, 3, 1)), flow_up
                ], 1))
        return flow