from typing import Callable, Dict, List, Optional, Type
import torch
import torch.nn as nn
from kornia.core import Module, Tensor, concatenate, stack
from kornia.utils.helpers import map_location_to_cpu
urls: Dict[str, str] = {}
urls["defmo_encoder"] = "http://ptak.felk.cvut.cz/personal/rozumden/defmo_saved_models/encoder_best.pt"
urls["defmo_rendering"] = "http://ptak.felk.cvut.cz/personal/rozumden/defmo_saved_models/rendering_best.pt"
# conv1x1, conv3x3, Bottleneck, ResNet are taken from:
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
"""1x1 convolution."""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
"""3x3 convolution with padding."""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation,
)
class Bottleneck(Module):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
expansion: int = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., Module]] = None,
) -> None:
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.0)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(Module):
def __init__(
self,
block: Type[Bottleneck],
layers: List[int],
num_classes: int = 1000,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation: Optional[List[bool]] = None,
norm_layer: Optional[Callable[..., Module]] = None,
) -> None:
super().__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError(
"replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation)
)
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck) and isinstance(m.bn3.weight, Tensor):
nn.init.constant_(m.bn3.weight, 0)
def _make_layer(
self, block: Type[Bottleneck], planes: int, blocks: int, stride: int = 1, dilate: bool = False
) -> nn.Sequential:
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion)
)
layers = []
layers.append(
block(
self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer
)
)
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation,
norm_layer=norm_layer,
)
)
return nn.Sequential(*layers)
def _forward_impl(self, x: Tensor) -> Tensor:
# See note [TorchScript super()]
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
class EncoderDeFMO(Module):
def __init__(self):
super().__init__()
model = ResNet(Bottleneck, [3, 4, 6, 3]) # ResNet50
modelc1 = nn.Sequential(*list(model.children())[:3])
modelc2 = nn.Sequential(*list(model.children())[4:8])
modelc1[0] = nn.Conv2d(6, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
self.net = nn.Sequential(modelc1, modelc2)
def forward(self, input_data: Tensor) -> Tensor:
return self.net(input_data)
class RenderingDeFMO(Module):
def __init__(self):
super().__init__()
self.tsr_steps: int = 24
model = nn.Sequential(
nn.Conv2d(2049, 1024, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
nn.ReLU(inplace=True),
Bottleneck(1024, 256),
nn.PixelShuffle(2),
Bottleneck(256, 64),
nn.PixelShuffle(2),
Bottleneck(64, 16),
nn.PixelShuffle(2),
nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1, bias=False),
nn.PixelShuffle(2),
nn.Conv2d(4, 4, kernel_size=3, stride=1, padding=1, bias=True),
nn.ReLU(inplace=True),
nn.Conv2d(4, 4, kernel_size=3, stride=1, padding=1, bias=True),
)
self.net = model
self.times = torch.linspace(0, 1, self.tsr_steps)
def forward(self, latent: Tensor) -> Tensor:
times = self.times.to(latent.device).unsqueeze(0).repeat(latent.shape[0], 1)
renders = []
for ki in range(times.shape[1]):
t_tensor = (
# TODO: replace by after deprecate pytorch 1.6
# times[list(range(times.shape[0])), ki]
times[[x for x in range(times.shape[0])], ki] # skipcq: PYL-R1721
.unsqueeze(-1)
.unsqueeze(-1)
.unsqueeze(-1)
.repeat(1, 1, latent.shape[2], latent.shape[3])
)
latenti = concatenate((t_tensor, latent), 1)
result = self.net(latenti)
renders.append(result)
renders_stacked = stack(renders, 1).contiguous()
renders_stacked[:, :, :4] = torch.sigmoid(renders_stacked[:, :, :4])
return renders_stacked
[docs]class DeFMO(Module):
"""Module that disentangle a fast-moving object from the background and performs deblurring.
This is based on the original code from paper "DeFMO: Deblurring and Shape Recovery
of Fast Moving Objects". See :cite:`DeFMO2021` for more details.
Args:
pretrained: Download and set pretrained weights to the model. Default: false.
Returns:
Temporal super-resolution without background.
Shape:
- Input: (B, 6, H, W)
- Output: (B, S, 4, H, W)
Examples:
>>> import kornia
>>> input = torch.rand(2, 6, 240, 320)
>>> defmo = kornia.feature.DeFMO()
>>> tsr_nobgr = defmo(input) # 2x24x4x240x320
"""
def __init__(self, pretrained: bool = False) -> None:
super().__init__()
self.encoder = EncoderDeFMO()
self.rendering = RenderingDeFMO()
# use torch.hub to load pretrained model
if pretrained:
pretrained_dict = torch.hub.load_state_dict_from_url(
urls['defmo_encoder'], map_location=map_location_to_cpu
)
self.encoder.load_state_dict(pretrained_dict, strict=True)
pretrained_dict_ren = torch.hub.load_state_dict_from_url(
urls['defmo_rendering'], map_location=map_location_to_cpu
)
self.rendering.load_state_dict(pretrained_dict_ren, strict=True)
self.eval()
[docs] def forward(self, input_data: Tensor) -> Tensor:
latent = self.encoder(input_data)
x_out = self.rendering(latent)
return x_out