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Source code for mmedit.models.backbones.sr_backbones.basicvsr_pp

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import constant_init
from mmcv.ops import ModulatedDeformConv2d, modulated_deform_conv2d
from mmcv.runner import load_checkpoint

from mmedit.models.backbones.sr_backbones.basicvsr_net import (
    ResidualBlocksWithInputConv, SPyNet)
from mmedit.models.common import PixelShufflePack, flow_warp
from mmedit.models.registry import BACKBONES
from mmedit.utils import get_root_logger

[docs]@BACKBONES.register_module() class BasicVSRPlusPlus(nn.Module): """BasicVSR++ network structure. Support either x4 upsampling or same size output. Paper: BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment Args: mid_channels (int, optional): Channel number of the intermediate features. Default: 64. num_blocks (int, optional): The number of residual blocks in each propagation branch. Default: 7. max_residue_magnitude (int): The maximum magnitude of the offset residue (Eq. 6 in paper). Default: 10. is_low_res_input (bool, optional): Whether the input is low-resolution or not. If False, the output resolution is equal to the input resolution. Default: True. spynet_pretrained (str, optional): Pre-trained model path of SPyNet. Default: None. cpu_cache_length (int, optional): When the length of sequence is larger than this value, the intermediate features are sent to CPU. This saves GPU memory, but slows down the inference speed. You can increase this number if you have a GPU with large memory. Default: 100. """ def __init__(self, mid_channels=64, num_blocks=7, max_residue_magnitude=10, is_low_res_input=True, spynet_pretrained=None, cpu_cache_length=100): super().__init__() self.mid_channels = mid_channels self.is_low_res_input = is_low_res_input self.cpu_cache_length = cpu_cache_length # optical flow self.spynet = SPyNet(pretrained=spynet_pretrained) # feature extraction module if is_low_res_input: self.feat_extract = ResidualBlocksWithInputConv(3, mid_channels, 5) else: self.feat_extract = nn.Sequential( nn.Conv2d(3, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(mid_channels, mid_channels, 3, 2, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), ResidualBlocksWithInputConv(mid_channels, mid_channels, 5)) # propagation branches self.deform_align = nn.ModuleDict() self.backbone = nn.ModuleDict() modules = ['backward_1', 'forward_1', 'backward_2', 'forward_2'] for i, module in enumerate(modules): self.deform_align[module] = SecondOrderDeformableAlignment( 2 * mid_channels, mid_channels, 3, padding=1, deform_groups=16, max_residue_magnitude=max_residue_magnitude) self.backbone[module] = ResidualBlocksWithInputConv( (2 + i) * mid_channels, mid_channels, num_blocks) # upsampling module self.reconstruction = ResidualBlocksWithInputConv( 5 * mid_channels, mid_channels, 5) self.upsample1 = PixelShufflePack( mid_channels, mid_channels, 2, upsample_kernel=3) self.upsample2 = PixelShufflePack( mid_channels, 64, 2, upsample_kernel=3) self.conv_hr = nn.Conv2d(64, 64, 3, 1, 1) self.conv_last = nn.Conv2d(64, 3, 3, 1, 1) self.img_upsample = nn.Upsample( scale_factor=4, mode='bilinear', align_corners=False) # activation function self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) # check if the sequence is augmented by flipping self.is_mirror_extended = False
[docs] def check_if_mirror_extended(self, lqs): """Check whether the input is a mirror-extended sequence. If mirror-extended, the i-th (i=0, ..., t-1) frame is equal to the (t-1-i)-th frame. Args: lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w). """ if lqs.size(1) % 2 == 0: lqs_1, lqs_2 = torch.chunk(lqs, 2, dim=1) if torch.norm(lqs_1 - lqs_2.flip(1)) == 0: self.is_mirror_extended = True
[docs] def compute_flow(self, lqs): """Compute optical flow using SPyNet for feature alignment. Note that if the input is an mirror-extended sequence, 'flows_forward' is not needed, since it is equal to 'flows_backward.flip(1)'. Args: lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w). Return: tuple(Tensor): Optical flow. 'flows_forward' corresponds to the flows used for forward-time propagation (current to previous). 'flows_backward' corresponds to the flows used for backward-time propagation (current to next). """ n, t, c, h, w = lqs.size() lqs_1 = lqs[:, :-1, :, :, :].reshape(-1, c, h, w) lqs_2 = lqs[:, 1:, :, :, :].reshape(-1, c, h, w) flows_backward = self.spynet(lqs_1, lqs_2).view(n, t - 1, 2, h, w) if self.is_mirror_extended: # flows_forward = flows_backward.flip(1) flows_forward = None else: flows_forward = self.spynet(lqs_2, lqs_1).view(n, t - 1, 2, h, w) if self.cpu_cache: flows_backward = flows_backward.cpu() flows_forward = flows_forward.cpu() return flows_forward, flows_backward
[docs] def propagate(self, feats, flows, module_name): """Propagate the latent features throughout the sequence. Args: feats dict(list[tensor]): Features from previous branches. Each component is a list of tensors with shape (n, c, h, w). flows (tensor): Optical flows with shape (n, t - 1, 2, h, w). module_name (str): The name of the propagation branches. Can either be 'backward_1', 'forward_1', 'backward_2', 'forward_2'. Return: dict(list[tensor]): A dictionary containing all the propagated features. Each key in the dictionary corresponds to a propagation branch, which is represented by a list of tensors. """ n, t, _, h, w = flows.size() frame_idx = range(0, t + 1) flow_idx = range(-1, t) mapping_idx = list(range(0, len(feats['spatial']))) mapping_idx += mapping_idx[::-1] if 'backward' in module_name: frame_idx = frame_idx[::-1] flow_idx = frame_idx feat_prop = flows.new_zeros(n, self.mid_channels, h, w) for i, idx in enumerate(frame_idx): feat_current = feats['spatial'][mapping_idx[idx]] if self.cpu_cache: feat_current = feat_current.cuda() feat_prop = feat_prop.cuda() # second-order deformable alignment if i > 0: flow_n1 = flows[:, flow_idx[i], :, :, :] if self.cpu_cache: flow_n1 = flow_n1.cuda() cond_n1 = flow_warp(feat_prop, flow_n1.permute(0, 2, 3, 1)) # initialize second-order features feat_n2 = torch.zeros_like(feat_prop) flow_n2 = torch.zeros_like(flow_n1) cond_n2 = torch.zeros_like(cond_n1) if i > 1: # second-order features feat_n2 = feats[module_name][-2] if self.cpu_cache: feat_n2 = feat_n2.cuda() flow_n2 = flows[:, flow_idx[i - 1], :, :, :] if self.cpu_cache: flow_n2 = flow_n2.cuda() flow_n2 = flow_n1 + flow_warp(flow_n2, flow_n1.permute(0, 2, 3, 1)) cond_n2 = flow_warp(feat_n2, flow_n2.permute(0, 2, 3, 1)) # flow-guided deformable convolution cond =[cond_n1, feat_current, cond_n2], dim=1) feat_prop =[feat_prop, feat_n2], dim=1) feat_prop = self.deform_align[module_name](feat_prop, cond, flow_n1, flow_n2) # concatenate and residual blocks feat = [feat_current] + [ feats[k][idx] for k in feats if k not in ['spatial', module_name] ] + [feat_prop] if self.cpu_cache: feat = [f.cuda() for f in feat] feat =, dim=1) feat_prop = feat_prop + self.backbone[module_name](feat) feats[module_name].append(feat_prop) if self.cpu_cache: feats[module_name][-1] = feats[module_name][-1].cpu() torch.cuda.empty_cache() if 'backward' in module_name: feats[module_name] = feats[module_name][::-1] return feats
[docs] def upsample(self, lqs, feats): """Compute the output image given the features. Args: lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w). feats (dict): The features from the propagation branches. Returns: Tensor: Output HR sequence with shape (n, t, c, 4h, 4w). """ outputs = [] num_outputs = len(feats['spatial']) mapping_idx = list(range(0, num_outputs)) mapping_idx += mapping_idx[::-1] for i in range(0, lqs.size(1)): hr = [feats[k].pop(0) for k in feats if k != 'spatial'] hr.insert(0, feats['spatial'][mapping_idx[i]]) hr =, dim=1) if self.cpu_cache: hr = hr.cuda() hr = self.reconstruction(hr) hr = self.lrelu(self.upsample1(hr)) hr = self.lrelu(self.upsample2(hr)) hr = self.lrelu(self.conv_hr(hr)) hr = self.conv_last(hr) if self.is_low_res_input: hr += self.img_upsample(lqs[:, i, :, :, :]) else: hr += lqs[:, i, :, :, :] if self.cpu_cache: hr = hr.cpu() torch.cuda.empty_cache() outputs.append(hr) return torch.stack(outputs, dim=1)
[docs] def forward(self, lqs): """Forward function for BasicVSR++. Args: lqs (tensor): Input low quality (LQ) sequence with shape (n, t, c, h, w). Returns: Tensor: Output HR sequence with shape (n, t, c, 4h, 4w). """ n, t, c, h, w = lqs.size() # whether to cache the features in CPU (no effect if using CPU) if t > self.cpu_cache_length and lqs.is_cuda: self.cpu_cache = True else: self.cpu_cache = False if self.is_low_res_input: lqs_downsample = lqs.clone() else: lqs_downsample = F.interpolate( lqs.view(-1, c, h, w), scale_factor=0.25, mode='bicubic').view(n, t, c, h // 4, w // 4) # check whether the input is an extended sequence self.check_if_mirror_extended(lqs) feats = {} # compute spatial features if self.cpu_cache: feats['spatial'] = [] for i in range(0, t): feat = self.feat_extract(lqs[:, i, :, :, :]).cpu() feats['spatial'].append(feat) torch.cuda.empty_cache() else: feats_ = self.feat_extract(lqs.view(-1, c, h, w)) h, w = feats_.shape[2:] feats_ = feats_.view(n, t, -1, h, w) feats['spatial'] = [feats_[:, i, :, :, :] for i in range(0, t)] # compute optical flow using the low-res inputs assert lqs_downsample.size(3) >= 64 and lqs_downsample.size(4) >= 64, ( 'The height and width of low-res inputs must be at least 64, ' f'but got {h} and {w}.') flows_forward, flows_backward = self.compute_flow(lqs_downsample) # feature propagation for iter_ in [1, 2]: for direction in ['backward', 'forward']: module = f'{direction}_{iter_}' feats[module] = [] if direction == 'backward': flows = flows_backward elif flows_forward is not None: flows = flows_forward else: flows = flows_backward.flip(1) feats = self.propagate(feats, flows, module) if self.cpu_cache: del flows torch.cuda.empty_cache() return self.upsample(lqs, feats)
[docs] def init_weights(self, pretrained=None, strict=True): """Init weights for models. Args: pretrained (str, optional): Path for pretrained weights. If given None, pretrained weights will not be loaded. Default: None. strict (bool, optional): Whether strictly load the pretrained model. Default: True. """ if isinstance(pretrained, str): logger = get_root_logger() load_checkpoint(self, pretrained, strict=strict, logger=logger) elif pretrained is not None: raise TypeError(f'"pretrained" must be a str or None. ' f'But received {type(pretrained)}.')
class SecondOrderDeformableAlignment(ModulatedDeformConv2d): """Second-order deformable alignment module. Args: in_channels (int): Same as nn.Conv2d. out_channels (int): Same as nn.Conv2d. kernel_size (int or tuple[int]): Same as nn.Conv2d. stride (int or tuple[int]): Same as nn.Conv2d. padding (int or tuple[int]): Same as nn.Conv2d. dilation (int or tuple[int]): Same as nn.Conv2d. groups (int): Same as nn.Conv2d. bias (bool or str): If specified as `auto`, it will be decided by the norm_cfg. Bias will be set as True if norm_cfg is None, otherwise False. max_residue_magnitude (int): The maximum magnitude of the offset residue (Eq. 6 in paper). Default: 10. """ def __init__(self, *args, **kwargs): self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10) super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs) self.conv_offset = nn.Sequential( nn.Conv2d(3 * self.out_channels + 4, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, 27 * self.deform_groups, 3, 1, 1), ) self.init_offset() def init_offset(self): constant_init(self.conv_offset[-1], val=0, bias=0) def forward(self, x, extra_feat, flow_1, flow_2): extra_feat =[extra_feat, flow_1, flow_2], dim=1) out = self.conv_offset(extra_feat) o1, o2, mask = torch.chunk(out, 3, dim=1) # offset offset = self.max_residue_magnitude * torch.tanh(, o2), dim=1)) offset_1, offset_2 = torch.chunk(offset, 2, dim=1) offset_1 = offset_1 + flow_1.flip(1).repeat(1, offset_1.size(1) // 2, 1, 1) offset_2 = offset_2 + flow_2.flip(1).repeat(1, offset_2.size(1) // 2, 1, 1) offset =[offset_1, offset_2], dim=1) # mask mask = torch.sigmoid(mask) return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups, self.deform_groups)
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