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| import torch | |
| from torch import nn as nn | |
| from torch.nn import functional as F | |
| from basicsr.utils.registry import ARCH_REGISTRY | |
| from .arch_util import flow_warp | |
| class BasicModule(nn.Module): | |
| """Basic module of SPyNet. | |
| Note that unlike the architecture in spynet_arch.py, the basic module | |
| here contains batch normalization. | |
| """ | |
| def __init__(self): | |
| super(BasicModule, self).__init__() | |
| self.basic_module = nn.Sequential( | |
| nn.Conv2d(in_channels=8, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False), | |
| nn.BatchNorm2d(32), nn.ReLU(inplace=True), | |
| nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3, bias=False), | |
| nn.BatchNorm2d(64), nn.ReLU(inplace=True), | |
| nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3, bias=False), | |
| nn.BatchNorm2d(32), nn.ReLU(inplace=True), | |
| nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3, bias=False), | |
| nn.BatchNorm2d(16), nn.ReLU(inplace=True), | |
| nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3)) | |
| 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 SPyNetTOF(nn.Module): | |
| """SPyNet architecture for TOF. | |
| Note that this implementation is specifically for TOFlow. Please use | |
| spynet_arch.py for general use. They differ 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 | |
| Args: | |
| load_path (str): Path for pretrained SPyNet. Default: None. | |
| """ | |
| def __init__(self, load_path=None): | |
| super(SPyNetTOF, self).__init__() | |
| self.basic_module = nn.ModuleList([BasicModule() for _ in range(4)]) | |
| if load_path: | |
| self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params']) | |
| 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 | |
| class TOFlow(nn.Module): | |
| """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): | |
| super(TOFlow, self).__init__() | |
| self.adapt_official_weights = adapt_official_weights | |
| self.ref_idx = 0 if adapt_official_weights else 3 | |
| self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) | |
| self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) | |
| # flow estimation module | |
| self.spynet = SPyNetTOF() | |
| # reconstruction module | |
| self.conv_1 = nn.Conv2d(3 * 7, 64, 9, 1, 4) | |
| self.conv_2 = nn.Conv2d(64, 64, 9, 1, 4) | |
| self.conv_3 = nn.Conv2d(64, 64, 1) | |
| self.conv_4 = nn.Conv2d(64, 3, 1) | |
| # activation function | |
| self.relu = nn.ReLU(inplace=True) | |
| def normalize(self, img): | |
| return (img - self.mean) / self.std | |
| def denormalize(self, img): | |
| return img * self.std + self.mean | |
| 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, num_lrs, _, h, w = lrs.size() | |
| lrs = self.normalize(lrs.view(-1, 3, h, w)) | |
| lrs = lrs.view(num_batches, num_lrs, 3, h, w) | |
| 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.conv_1(hr)) | |
| hr = self.relu(self.conv_2(hr)) | |
| hr = self.relu(self.conv_3(hr)) | |
| hr = self.conv_4(hr) + lr_ref | |
| return self.denormalize(hr) | |