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import math
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 for SpyNet.
"""
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), nn.ReLU(inplace=False),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
nn.Conv2d(in_channels=64, out_channels=32, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
nn.Conv2d(in_channels=32, out_channels=16, kernel_size=7, stride=1, padding=3), nn.ReLU(inplace=False),
nn.Conv2d(in_channels=16, out_channels=2, kernel_size=7, stride=1, padding=3))
def forward(self, tensor_input):
return self.basic_module(tensor_input)
@ARCH_REGISTRY.register()
class SpyNet(nn.Module):
"""SpyNet architecture.
Args:
load_path (str): path for pretrained SpyNet. Default: None.
"""
def __init__(self, load_path=None):
super(SpyNet, self).__init__()
self.basic_module = nn.ModuleList([BasicModule() for _ in range(6)])
if load_path:
self.load_state_dict(torch.load(load_path, map_location=lambda storage, loc: storage)['params'])
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))
def preprocess(self, tensor_input):
tensor_output = (tensor_input - self.mean) / self.std
return tensor_output
def process(self, ref, supp):
flow = []
ref = [self.preprocess(ref)]
supp = [self.preprocess(supp)]
for level in range(5):
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 = ref[0].new_zeros(
[ref[0].size(0), 2,
int(math.floor(ref[0].size(2) / 2.0)),
int(math.floor(ref[0].size(3) / 2.0))])
for level in range(len(ref)):
upsampled_flow = F.interpolate(input=flow, scale_factor=2, mode='bilinear', align_corners=True) * 2.0
if upsampled_flow.size(2) != ref[level].size(2):
upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 0, 0, 1], mode='replicate')
if upsampled_flow.size(3) != ref[level].size(3):
upsampled_flow = F.pad(input=upsampled_flow, pad=[0, 1, 0, 0], mode='replicate')
flow = self.basic_module[level](torch.cat([
ref[level],
flow_warp(
supp[level], upsampled_flow.permute(0, 2, 3, 1), interp_mode='bilinear', padding_mode='border'),
upsampled_flow
], 1)) + upsampled_flow
return flow
def forward(self, ref, supp):
assert ref.size() == supp.size()
h, w = ref.size(2), ref.size(3)
w_floor = math.floor(math.ceil(w / 32.0) * 32.0)
h_floor = math.floor(math.ceil(h / 32.0) * 32.0)
ref = F.interpolate(input=ref, size=(h_floor, w_floor), mode='bilinear', align_corners=False)
supp = F.interpolate(input=supp, size=(h_floor, w_floor), mode='bilinear', align_corners=False)
flow = F.interpolate(input=self.process(ref, supp), size=(h, w), mode='bilinear', align_corners=False)
flow[:, 0, :, :] *= float(w) / float(w_floor)
flow[:, 1, :, :] *= float(h) / float(h_floor)
return flow
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