Source code for torchvision.models.optical_flow.raft
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.nn.modules.batchnorm import BatchNorm2d
from torch.nn.modules.instancenorm import InstanceNorm2d
from torchvision.ops import Conv2dNormActivation
from ...transforms._presets import OpticalFlow
from ...utils import _log_api_usage_once
from .._api import register_model, Weights, WeightsEnum
from .._utils import handle_legacy_interface
from ._utils import grid_sample, make_coords_grid, upsample_flow
__all__ = (
"RAFT",
"raft_large",
"raft_small",
"Raft_Large_Weights",
"Raft_Small_Weights",
)
class ResidualBlock(nn.Module):
"""Slightly modified Residual block with extra relu and biases."""
def __init__(self, in_channels, out_channels, *, norm_layer, stride=1, always_project: bool = False):
super().__init__()
# Note regarding bias=True:
# Usually we can pass bias=False in conv layers followed by a norm layer.
# But in the RAFT training reference, the BatchNorm2d layers are only activated for the first dataset,
# and frozen for the rest of the training process (i.e. set as eval()). The bias term is thus still useful
# for the rest of the datasets. Technically, we could remove the bias for other norm layers like Instance norm
# because these aren't frozen, but we don't bother (also, we wouldn't be able to load the original weights).
self.convnormrelu1 = Conv2dNormActivation(
in_channels, out_channels, norm_layer=norm_layer, kernel_size=3, stride=stride, bias=True
)
self.convnormrelu2 = Conv2dNormActivation(
out_channels, out_channels, norm_layer=norm_layer, kernel_size=3, bias=True
)
# make mypy happy
self.downsample: nn.Module
if stride == 1 and not always_project:
self.downsample = nn.Identity()
else:
self.downsample = Conv2dNormActivation(
in_channels,
out_channels,
norm_layer=norm_layer,
kernel_size=1,
stride=stride,
bias=True,
activation_layer=None,
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
y = x
y = self.convnormrelu1(y)
y = self.convnormrelu2(y)
x = self.downsample(x)
return self.relu(x + y)
class BottleneckBlock(nn.Module):
"""Slightly modified BottleNeck block (extra relu and biases)"""
def __init__(self, in_channels, out_channels, *, norm_layer, stride=1):
super().__init__()
# See note in ResidualBlock for the reason behind bias=True
self.convnormrelu1 = Conv2dNormActivation(
in_channels, out_channels // 4, norm_layer=norm_layer, kernel_size=1, bias=True
)
self.convnormrelu2 = Conv2dNormActivation(
out_channels // 4, out_channels // 4, norm_layer=norm_layer, kernel_size=3, stride=stride, bias=True
)
self.convnormrelu3 = Conv2dNormActivation(
out_channels // 4, out_channels, norm_layer=norm_layer, kernel_size=1, bias=True
)
self.relu = nn.ReLU(inplace=True)
if stride == 1:
self.downsample = nn.Identity()
else:
self.downsample = Conv2dNormActivation(
in_channels,
out_channels,
norm_layer=norm_layer,
kernel_size=1,
stride=stride,
bias=True,
activation_layer=None,
)
def forward(self, x):
y = x
y = self.convnormrelu1(y)
y = self.convnormrelu2(y)
y = self.convnormrelu3(y)
x = self.downsample(x)
return self.relu(x + y)
class FeatureEncoder(nn.Module):
"""The feature encoder, used both as the actual feature encoder, and as the context encoder.
It must downsample its input by 8.
"""
def __init__(
self, *, block=ResidualBlock, layers=(64, 64, 96, 128, 256), strides=(2, 1, 2, 2), norm_layer=nn.BatchNorm2d
):
super().__init__()
if len(layers) != 5:
raise ValueError(f"The expected number of layers is 5, instead got {len(layers)}")
# See note in ResidualBlock for the reason behind bias=True
self.convnormrelu = Conv2dNormActivation(
3, layers[0], norm_layer=norm_layer, kernel_size=7, stride=strides[0], bias=True
)
self.layer1 = self._make_2_blocks(block, layers[0], layers[1], norm_layer=norm_layer, first_stride=strides[1])
self.layer2 = self._make_2_blocks(block, layers[1], layers[2], norm_layer=norm_layer, first_stride=strides[2])
self.layer3 = self._make_2_blocks(block, layers[2], layers[3], norm_layer=norm_layer, first_stride=strides[3])
self.conv = nn.Conv2d(layers[3], layers[4], kernel_size=1)
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.InstanceNorm2d)):
if m.weight is not None:
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
num_downsamples = len(list(filter(lambda s: s == 2, strides)))
self.output_dim = layers[-1]
self.downsample_factor = 2**num_downsamples
def _make_2_blocks(self, block, in_channels, out_channels, norm_layer, first_stride):
block1 = block(in_channels, out_channels, norm_layer=norm_layer, stride=first_stride)
block2 = block(out_channels, out_channels, norm_layer=norm_layer, stride=1)
return nn.Sequential(block1, block2)
def forward(self, x):
x = self.convnormrelu(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.conv(x)
return x
class MotionEncoder(nn.Module):
"""The motion encoder, part of the update block.
Takes the current predicted flow and the correlation features as input and returns an encoded version of these.
"""
def __init__(self, *, in_channels_corr, corr_layers=(256, 192), flow_layers=(128, 64), out_channels=128):
super().__init__()
if len(flow_layers) != 2:
raise ValueError(f"The expected number of flow_layers is 2, instead got {len(flow_layers)}")
if len(corr_layers) not in (1, 2):
raise ValueError(f"The number of corr_layers should be 1 or 2, instead got {len(corr_layers)}")
self.convcorr1 = Conv2dNormActivation(in_channels_corr, corr_layers[0], norm_layer=None, kernel_size=1)
if len(corr_layers) == 2:
self.convcorr2 = Conv2dNormActivation(corr_layers[0], corr_layers[1], norm_layer=None, kernel_size=3)
else:
self.convcorr2 = nn.Identity()
self.convflow1 = Conv2dNormActivation(2, flow_layers[0], norm_layer=None, kernel_size=7)
self.convflow2 = Conv2dNormActivation(flow_layers[0], flow_layers[1], norm_layer=None, kernel_size=3)
# out_channels - 2 because we cat the flow (2 channels) at the end
self.conv = Conv2dNormActivation(
corr_layers[-1] + flow_layers[-1], out_channels - 2, norm_layer=None, kernel_size=3
)
self.out_channels = out_channels
def forward(self, flow, corr_features):
corr = self.convcorr1(corr_features)
corr = self.convcorr2(corr)
flow_orig = flow
flow = self.convflow1(flow)
flow = self.convflow2(flow)
corr_flow = torch.cat([corr, flow], dim=1)
corr_flow = self.conv(corr_flow)
return torch.cat([corr_flow, flow_orig], dim=1)
class ConvGRU(nn.Module):
"""Convolutional Gru unit."""
def __init__(self, *, input_size, hidden_size, kernel_size, padding):
super().__init__()
self.convz = nn.Conv2d(hidden_size + input_size, hidden_size, kernel_size=kernel_size, padding=padding)
self.convr = nn.Conv2d(hidden_size + input_size, hidden_size, kernel_size=kernel_size, padding=padding)
self.convq = nn.Conv2d(hidden_size + input_size, hidden_size, kernel_size=kernel_size, padding=padding)
def forward(self, h, x):
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz(hx))
r = torch.sigmoid(self.convr(hx))
q = torch.tanh(self.convq(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
return h
def _pass_through_h(h, _):
# Declared here for torchscript
return h
class RecurrentBlock(nn.Module):
"""Recurrent block, part of the update block.
Takes the current hidden state and the concatenation of (motion encoder output, context) as input.
Returns an updated hidden state.
"""
def __init__(self, *, input_size, hidden_size, kernel_size=((1, 5), (5, 1)), padding=((0, 2), (2, 0))):
super().__init__()
if len(kernel_size) != len(padding):
raise ValueError(
f"kernel_size should have the same length as padding, instead got len(kernel_size) = {len(kernel_size)} and len(padding) = {len(padding)}"
)
if len(kernel_size) not in (1, 2):
raise ValueError(f"kernel_size should either 1 or 2, instead got {len(kernel_size)}")
self.convgru1 = ConvGRU(
input_size=input_size, hidden_size=hidden_size, kernel_size=kernel_size[0], padding=padding[0]
)
if len(kernel_size) == 2:
self.convgru2 = ConvGRU(
input_size=input_size, hidden_size=hidden_size, kernel_size=kernel_size[1], padding=padding[1]
)
else:
self.convgru2 = _pass_through_h
self.hidden_size = hidden_size
def forward(self, h, x):
h = self.convgru1(h, x)
h = self.convgru2(h, x)
return h
class FlowHead(nn.Module):
"""Flow head, part of the update block.
Takes the hidden state of the recurrent unit as input, and outputs the predicted "delta flow".
"""
def __init__(self, *, in_channels, hidden_size):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, hidden_size, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_size, 2, 3, padding=1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.conv2(self.relu(self.conv1(x)))
class UpdateBlock(nn.Module):
"""The update block which contains the motion encoder, the recurrent block, and the flow head.
It must expose a ``hidden_state_size`` attribute which is the hidden state size of its recurrent block.
"""
def __init__(self, *, motion_encoder, recurrent_block, flow_head):
super().__init__()
self.motion_encoder = motion_encoder
self.recurrent_block = recurrent_block
self.flow_head = flow_head
self.hidden_state_size = recurrent_block.hidden_size
def forward(self, hidden_state, context, corr_features, flow):
motion_features = self.motion_encoder(flow, corr_features)
x = torch.cat([context, motion_features], dim=1)
hidden_state = self.recurrent_block(hidden_state, x)
delta_flow = self.flow_head(hidden_state)
return hidden_state, delta_flow
class MaskPredictor(nn.Module):
"""Mask predictor to be used when upsampling the predicted flow.
It takes the hidden state of the recurrent unit as input and outputs the mask.
This is not used in the raft-small model.
"""
def __init__(self, *, in_channels, hidden_size, multiplier=0.25):
super().__init__()
self.convrelu = Conv2dNormActivation(in_channels, hidden_size, norm_layer=None, kernel_size=3)
# 8 * 8 * 9 because the predicted flow is downsampled by 8, from the downsampling of the initial FeatureEncoder,
# and we interpolate with all 9 surrounding neighbors. See paper and appendix B.
self.conv = nn.Conv2d(hidden_size, 8 * 8 * 9, 1, padding=0)
# In the original code, they use a factor of 0.25 to "downweight the gradients" of that branch.
# See e.g. https://github.com/princeton-vl/RAFT/issues/119#issuecomment-953950419
# or https://github.com/princeton-vl/RAFT/issues/24.
# It doesn't seem to affect epe significantly and can likely be set to 1.
self.multiplier = multiplier
def forward(self, x):
x = self.convrelu(x)
x = self.conv(x)
return self.multiplier * x
class CorrBlock(nn.Module):
"""The correlation block.
Creates a correlation pyramid with ``num_levels`` levels from the outputs of the feature encoder,
and then indexes from this pyramid to create correlation features.
The "indexing" of a given centroid pixel x' is done by concatenating its surrounding neighbors that
are within a ``radius``, according to the infinity norm (see paper section 3.2).
Note: typo in the paper, it should be infinity norm, not 1-norm.
"""
def __init__(self, *, num_levels: int = 4, radius: int = 4):
super().__init__()
self.num_levels = num_levels
self.radius = radius
self.corr_pyramid: List[Tensor] = [torch.tensor(0)] # useless, but torchscript is otherwise confused :')
# The neighborhood of a centroid pixel x' is {x' + delta, ||delta||_inf <= radius}
# so it's a square surrounding x', and its sides have a length of 2 * radius + 1
# The paper claims that it's ||.||_1 instead of ||.||_inf but it's a typo:
# https://github.com/princeton-vl/RAFT/issues/122
self.out_channels = num_levels * (2 * radius + 1) ** 2
def build_pyramid(self, fmap1, fmap2):
"""Build the correlation pyramid from two feature maps.
The correlation volume is first computed as the dot product of each pair (pixel_in_fmap1, pixel_in_fmap2)
The last 2 dimensions of the correlation volume are then pooled num_levels times at different resolutions
to build the correlation pyramid.
"""
if fmap1.shape != fmap2.shape:
raise ValueError(
f"Input feature maps should have the same shape, instead got {fmap1.shape} (fmap1.shape) != {fmap2.shape} (fmap2.shape)"
)
corr_volume = self._compute_corr_volume(fmap1, fmap2)
batch_size, h, w, num_channels, _, _ = corr_volume.shape # _, _ = h, w
corr_volume = corr_volume.reshape(batch_size * h * w, num_channels, h, w)
self.corr_pyramid = [corr_volume]
for _ in range(self.num_levels - 1):
corr_volume = F.avg_pool2d(corr_volume, kernel_size=2, stride=2)
self.corr_pyramid.append(corr_volume)
def index_pyramid(self, centroids_coords):
"""Return correlation features by indexing from the pyramid."""
neighborhood_side_len = 2 * self.radius + 1 # see note in __init__ about out_channels
di = torch.linspace(-self.radius, self.radius, neighborhood_side_len)
dj = torch.linspace(-self.radius, self.radius, neighborhood_side_len)
delta = torch.stack(torch.meshgrid(di, dj, indexing="ij"), dim=-1).to(centroids_coords.device)
delta = delta.view(1, neighborhood_side_len, neighborhood_side_len, 2)
batch_size, _, h, w = centroids_coords.shape # _ = 2
centroids_coords = centroids_coords.permute(0, 2, 3, 1).reshape(batch_size * h * w, 1, 1, 2)
indexed_pyramid = []
for corr_volume in self.corr_pyramid:
sampling_coords = centroids_coords + delta # end shape is (batch_size * h * w, side_len, side_len, 2)
indexed_corr_volume = grid_sample(corr_volume, sampling_coords, align_corners=True, mode="bilinear").view(
batch_size, h, w, -1
)
indexed_pyramid.append(indexed_corr_volume)
centroids_coords = centroids_coords / 2
corr_features = torch.cat(indexed_pyramid, dim=-1).permute(0, 3, 1, 2).contiguous()
expected_output_shape = (batch_size, self.out_channels, h, w)
if corr_features.shape != expected_output_shape:
raise ValueError(
f"Output shape of index pyramid is incorrect. Should be {expected_output_shape}, got {corr_features.shape}"
)
return corr_features
def _compute_corr_volume(self, fmap1, fmap2):
batch_size, num_channels, h, w = fmap1.shape
fmap1 = fmap1.view(batch_size, num_channels, h * w)
fmap2 = fmap2.view(batch_size, num_channels, h * w)
corr = torch.matmul(fmap1.transpose(1, 2), fmap2)
corr = corr.view(batch_size, h, w, 1, h, w)
return corr / torch.sqrt(torch.tensor(num_channels))
class RAFT(nn.Module):
def __init__(self, *, feature_encoder, context_encoder, corr_block, update_block, mask_predictor=None):
"""RAFT model from
`RAFT: Recurrent All Pairs Field Transforms for Optical Flow <https://arxiv.org/abs/2003.12039>`_.
args:
feature_encoder (nn.Module): The feature encoder. It must downsample the input by 8.
Its input is the concatenation of ``image1`` and ``image2``.
context_encoder (nn.Module): The context encoder. It must downsample the input by 8.
Its input is ``image1``. As in the original implementation, its output will be split into 2 parts:
- one part will be used as the actual "context", passed to the recurrent unit of the ``update_block``
- one part will be used to initialize the hidden state of the recurrent unit of
the ``update_block``
These 2 parts are split according to the ``hidden_state_size`` of the ``update_block``, so the output
of the ``context_encoder`` must be strictly greater than ``hidden_state_size``.
corr_block (nn.Module): The correlation block, which creates a correlation pyramid from the output of the
``feature_encoder``, and then indexes from this pyramid to create correlation features. It must expose
2 methods:
- a ``build_pyramid`` method that takes ``feature_map_1`` and ``feature_map_2`` as input (these are the
output of the ``feature_encoder``).
- a ``index_pyramid`` method that takes the coordinates of the centroid pixels as input, and returns
the correlation features. See paper section 3.2.
It must expose an ``out_channels`` attribute.
update_block (nn.Module): The update block, which contains the motion encoder, the recurrent unit, and the
flow head. It takes as input the hidden state of its recurrent unit, the context, the correlation
features, and the current predicted flow. It outputs an updated hidden state, and the ``delta_flow``
prediction (see paper appendix A). It must expose a ``hidden_state_size`` attribute.
mask_predictor (nn.Module, optional): Predicts the mask that will be used to upsample the predicted flow.
The output channel must be 8 * 8 * 9 - see paper section 3.3, and Appendix B.
If ``None`` (default), the flow is upsampled using interpolation.
"""
super().__init__()
_log_api_usage_once(self)
self.feature_encoder = feature_encoder
self.context_encoder = context_encoder
self.corr_block = corr_block
self.update_block = update_block
self.mask_predictor = mask_predictor
if not hasattr(self.update_block, "hidden_state_size"):
raise ValueError("The update_block parameter should expose a 'hidden_state_size' attribute.")
def forward(self, image1, image2, num_flow_updates: int = 12):
batch_size, _, h, w = image1.shape
if (h, w) != image2.shape[-2:]:
raise ValueError(f"input images should have the same shape, instead got ({h}, {w}) != {image2.shape[-2:]}")
if not (h % 8 == 0) and (w % 8 == 0):
raise ValueError(f"input image H and W should be divisible by 8, instead got {h} (h) and {w} (w)")
fmaps = self.feature_encoder(torch.cat([image1, image2], dim=0))
fmap1, fmap2 = torch.chunk(fmaps, chunks=2, dim=0)
if fmap1.shape[-2:] != (h // 8, w // 8):
raise ValueError("The feature encoder should downsample H and W by 8")
self.corr_block.build_pyramid(fmap1, fmap2)
context_out = self.context_encoder(image1)
if context_out.shape[-2:] != (h // 8, w // 8):
raise ValueError("The context encoder should downsample H and W by 8")
# As in the original paper, the actual output of the context encoder is split in 2 parts:
# - one part is used to initialize the hidden state of the recurent units of the update block
# - the rest is the "actual" context.
hidden_state_size = self.update_block.hidden_state_size
out_channels_context = context_out.shape[1] - hidden_state_size
if out_channels_context <= 0:
raise ValueError(
f"The context encoder outputs {context_out.shape[1]} channels, but it should have at strictly more than hidden_state={hidden_state_size} channels"
)
hidden_state, context = torch.split(context_out, [hidden_state_size, out_channels_context], dim=1)
hidden_state = torch.tanh(hidden_state)
context = F.relu(context)
coords0 = make_coords_grid(batch_size, h // 8, w // 8).to(fmap1.device)
coords1 = make_coords_grid(batch_size, h // 8, w // 8).to(fmap1.device)
flow_predictions = []
for _ in range(num_flow_updates):
coords1 = coords1.detach() # Don't backpropagate gradients through this branch, see paper
corr_features = self.corr_block.index_pyramid(centroids_coords=coords1)
flow = coords1 - coords0
hidden_state, delta_flow = self.update_block(hidden_state, context, corr_features, flow)
coords1 = coords1 + delta_flow
up_mask = None if self.mask_predictor is None else self.mask_predictor(hidden_state)
upsampled_flow = upsample_flow(flow=(coords1 - coords0), up_mask=up_mask)
flow_predictions.append(upsampled_flow)
return flow_predictions
_COMMON_META = {
"min_size": (128, 128),
}
[docs]class Raft_Large_Weights(WeightsEnum):
"""The metrics reported here are as follows.
``epe`` is the "end-point-error" and indicates how far (in pixels) the
predicted flow is from its true value. This is averaged over all pixels
of all images. ``per_image_epe`` is similar, but the average is different:
the epe is first computed on each image independently, and then averaged
over all images. This corresponds to "Fl-epe" (sometimes written "F1-epe")
in the original paper, and it's only used on Kitti. ``fl-all`` is also a
Kitti-specific metric, defined by the author of the dataset and used for the
Kitti leaderboard. It corresponds to the average of pixels whose epe is
either <3px, or <5% of flow's 2-norm.
"""
C_T_V1 = Weights(
# Weights ported from https://github.com/princeton-vl/RAFT
url="https://download.pytorch.org/models/raft_large_C_T_V1-22a6c225.pth",
transforms=OpticalFlow,
meta={
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/princeton-vl/RAFT",
"_metrics": {
"Sintel-Train-Cleanpass": {"epe": 1.4411},
"Sintel-Train-Finalpass": {"epe": 2.7894},
"Kitti-Train": {"per_image_epe": 5.0172, "fl_all": 17.4506},
},
"_ops": 211.007,
"_file_size": 20.129,
"_docs": """These weights were ported from the original paper. They
are trained on :class:`~torchvision.datasets.FlyingChairs` +
:class:`~torchvision.datasets.FlyingThings3D`.""",
},
)
C_T_V2 = Weights(
url="https://download.pytorch.org/models/raft_large_C_T_V2-1bb1363a.pth",
transforms=OpticalFlow,
meta={
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow",
"_metrics": {
"Sintel-Train-Cleanpass": {"epe": 1.3822},
"Sintel-Train-Finalpass": {"epe": 2.7161},
"Kitti-Train": {"per_image_epe": 4.5118, "fl_all": 16.0679},
},
"_ops": 211.007,
"_file_size": 20.129,
"_docs": """These weights were trained from scratch on
:class:`~torchvision.datasets.FlyingChairs` +
:class:`~torchvision.datasets.FlyingThings3D`.""",
},
)
C_T_SKHT_V1 = Weights(
# Weights ported from https://github.com/princeton-vl/RAFT
url="https://download.pytorch.org/models/raft_large_C_T_SKHT_V1-0b8c9e55.pth",
transforms=OpticalFlow,
meta={
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/princeton-vl/RAFT",
"_metrics": {
"Sintel-Test-Cleanpass": {"epe": 1.94},
"Sintel-Test-Finalpass": {"epe": 3.18},
},
"_ops": 211.007,
"_file_size": 20.129,
"_docs": """
These weights were ported from the original paper. They are
trained on :class:`~torchvision.datasets.FlyingChairs` +
:class:`~torchvision.datasets.FlyingThings3D` and fine-tuned on
Sintel. The Sintel fine-tuning step is a combination of
:class:`~torchvision.datasets.Sintel`,
:class:`~torchvision.datasets.KittiFlow`,
:class:`~torchvision.datasets.HD1K`, and
:class:`~torchvision.datasets.FlyingThings3D` (clean pass).
""",
},
)
C_T_SKHT_V2 = Weights(
url="https://download.pytorch.org/models/raft_large_C_T_SKHT_V2-ff5fadd5.pth",
transforms=OpticalFlow,
meta={
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow",
"_metrics": {
"Sintel-Test-Cleanpass": {"epe": 1.819},
"Sintel-Test-Finalpass": {"epe": 3.067},
},
"_ops": 211.007,
"_file_size": 20.129,
"_docs": """
These weights were trained from scratch. They are
pre-trained on :class:`~torchvision.datasets.FlyingChairs` +
:class:`~torchvision.datasets.FlyingThings3D` and then
fine-tuned on Sintel. The Sintel fine-tuning step is a
combination of :class:`~torchvision.datasets.Sintel`,
:class:`~torchvision.datasets.KittiFlow`,
:class:`~torchvision.datasets.HD1K`, and
:class:`~torchvision.datasets.FlyingThings3D` (clean pass).
""",
},
)
C_T_SKHT_K_V1 = Weights(
# Weights ported from https://github.com/princeton-vl/RAFT
url="https://download.pytorch.org/models/raft_large_C_T_SKHT_K_V1-4a6a5039.pth",
transforms=OpticalFlow,
meta={
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/princeton-vl/RAFT",
"_metrics": {
"Kitti-Test": {"fl_all": 5.10},
},
"_ops": 211.007,
"_file_size": 20.129,
"_docs": """
These weights were ported from the original paper. They are
pre-trained on :class:`~torchvision.datasets.FlyingChairs` +
:class:`~torchvision.datasets.FlyingThings3D`,
fine-tuned on Sintel, and then fine-tuned on
:class:`~torchvision.datasets.KittiFlow`. The Sintel fine-tuning
step was described above.
""",
},
)
C_T_SKHT_K_V2 = Weights(
url="https://download.pytorch.org/models/raft_large_C_T_SKHT_K_V2-b5c70766.pth",
transforms=OpticalFlow,
meta={
**_COMMON_META,
"num_params": 5257536,
"recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow",
"_metrics": {
"Kitti-Test": {"fl_all": 5.19},
},
"_ops": 211.007,
"_file_size": 20.129,
"_docs": """
These weights were trained from scratch. They are
pre-trained on :class:`~torchvision.datasets.FlyingChairs` +
:class:`~torchvision.datasets.FlyingThings3D`,
fine-tuned on Sintel, and then fine-tuned on
:class:`~torchvision.datasets.KittiFlow`. The Sintel fine-tuning
step was described above.
""",
},
)
DEFAULT = C_T_SKHT_V2
[docs]class Raft_Small_Weights(WeightsEnum):
"""The metrics reported here are as follows.
``epe`` is the "end-point-error" and indicates how far (in pixels) the
predicted flow is from its true value. This is averaged over all pixels
of all images. ``per_image_epe`` is similar, but the average is different:
the epe is first computed on each image independently, and then averaged
over all images. This corresponds to "Fl-epe" (sometimes written "F1-epe")
in the original paper, and it's only used on Kitti. ``fl-all`` is also a
Kitti-specific metric, defined by the author of the dataset and used for the
Kitti leaderboard. It corresponds to the average of pixels whose epe is
either <3px, or <5% of flow's 2-norm.
"""
C_T_V1 = Weights(
# Weights ported from https://github.com/princeton-vl/RAFT
url="https://download.pytorch.org/models/raft_small_C_T_V1-ad48884c.pth",
transforms=OpticalFlow,
meta={
**_COMMON_META,
"num_params": 990162,
"recipe": "https://github.com/princeton-vl/RAFT",
"_metrics": {
"Sintel-Train-Cleanpass": {"epe": 2.1231},
"Sintel-Train-Finalpass": {"epe": 3.2790},
"Kitti-Train": {"per_image_epe": 7.6557, "fl_all": 25.2801},
},
"_ops": 47.655,
"_file_size": 3.821,
"_docs": """These weights were ported from the original paper. They
are trained on :class:`~torchvision.datasets.FlyingChairs` +
:class:`~torchvision.datasets.FlyingThings3D`.""",
},
)
C_T_V2 = Weights(
url="https://download.pytorch.org/models/raft_small_C_T_V2-01064c6d.pth",
transforms=OpticalFlow,
meta={
**_COMMON_META,
"num_params": 990162,
"recipe": "https://github.com/pytorch/vision/tree/main/references/optical_flow",
"_metrics": {
"Sintel-Train-Cleanpass": {"epe": 1.9901},
"Sintel-Train-Finalpass": {"epe": 3.2831},
"Kitti-Train": {"per_image_epe": 7.5978, "fl_all": 25.2369},
},
"_ops": 47.655,
"_file_size": 3.821,
"_docs": """These weights were trained from scratch on
:class:`~torchvision.datasets.FlyingChairs` +
:class:`~torchvision.datasets.FlyingThings3D`.""",
},
)
DEFAULT = C_T_V2
def _raft(
*,
weights=None,
progress=False,
# Feature encoder
feature_encoder_layers,
feature_encoder_block,
feature_encoder_norm_layer,
# Context encoder
context_encoder_layers,
context_encoder_block,
context_encoder_norm_layer,
# Correlation block
corr_block_num_levels,
corr_block_radius,
# Motion encoder
motion_encoder_corr_layers,
motion_encoder_flow_layers,
motion_encoder_out_channels,
# Recurrent block
recurrent_block_hidden_state_size,
recurrent_block_kernel_size,
recurrent_block_padding,
# Flow Head
flow_head_hidden_size,
# Mask predictor
use_mask_predictor,
**kwargs,
):
feature_encoder = kwargs.pop("feature_encoder", None) or FeatureEncoder(
block=feature_encoder_block, layers=feature_encoder_layers, norm_layer=feature_encoder_norm_layer
)
context_encoder = kwargs.pop("context_encoder", None) or FeatureEncoder(
block=context_encoder_block, layers=context_encoder_layers, norm_layer=context_encoder_norm_layer
)
corr_block = kwargs.pop("corr_block", None) or CorrBlock(num_levels=corr_block_num_levels, radius=corr_block_radius)
update_block = kwargs.pop("update_block", None)
if update_block is None:
motion_encoder = MotionEncoder(
in_channels_corr=corr_block.out_channels,
corr_layers=motion_encoder_corr_layers,
flow_layers=motion_encoder_flow_layers,
out_channels=motion_encoder_out_channels,
)
# See comments in forward pass of RAFT class about why we split the output of the context encoder
out_channels_context = context_encoder_layers[-1] - recurrent_block_hidden_state_size
recurrent_block = RecurrentBlock(
input_size=motion_encoder.out_channels + out_channels_context,
hidden_size=recurrent_block_hidden_state_size,
kernel_size=recurrent_block_kernel_size,
padding=recurrent_block_padding,
)
flow_head = FlowHead(in_channels=recurrent_block_hidden_state_size, hidden_size=flow_head_hidden_size)
update_block = UpdateBlock(motion_encoder=motion_encoder, recurrent_block=recurrent_block, flow_head=flow_head)
mask_predictor = kwargs.pop("mask_predictor", None)
if mask_predictor is None and use_mask_predictor:
mask_predictor = MaskPredictor(
in_channels=recurrent_block_hidden_state_size,
hidden_size=256,
multiplier=0.25, # See comment in MaskPredictor about this
)
model = RAFT(
feature_encoder=feature_encoder,
context_encoder=context_encoder,
corr_block=corr_block,
update_block=update_block,
mask_predictor=mask_predictor,
**kwargs, # not really needed, all params should be consumed by now
)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress))
return model
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Raft_Large_Weights.C_T_SKHT_V2))
def raft_large(*, weights: Optional[Raft_Large_Weights] = None, progress=True, **kwargs) -> RAFT:
"""RAFT model from
`RAFT: Recurrent All Pairs Field Transforms for Optical Flow <https://arxiv.org/abs/2003.12039>`_.
Please see the example below for a tutorial on how to use this model.
Args:
weights(:class:`~torchvision.models.optical_flow.Raft_Large_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.optical_flow.Raft_Large_Weights`
below for more details, and possible values. By default, no
pre-trained weights are used.
progress (bool): If True, displays a progress bar of the download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.optical_flow.RAFT``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/optical_flow/raft.py>`_
for more details about this class.
.. autoclass:: torchvision.models.optical_flow.Raft_Large_Weights
:members:
"""
weights = Raft_Large_Weights.verify(weights)
return _raft(
weights=weights,
progress=progress,
# Feature encoder
feature_encoder_layers=(64, 64, 96, 128, 256),
feature_encoder_block=ResidualBlock,
feature_encoder_norm_layer=InstanceNorm2d,
# Context encoder
context_encoder_layers=(64, 64, 96, 128, 256),
context_encoder_block=ResidualBlock,
context_encoder_norm_layer=BatchNorm2d,
# Correlation block
corr_block_num_levels=4,
corr_block_radius=4,
# Motion encoder
motion_encoder_corr_layers=(256, 192),
motion_encoder_flow_layers=(128, 64),
motion_encoder_out_channels=128,
# Recurrent block
recurrent_block_hidden_state_size=128,
recurrent_block_kernel_size=((1, 5), (5, 1)),
recurrent_block_padding=((0, 2), (2, 0)),
# Flow head
flow_head_hidden_size=256,
# Mask predictor
use_mask_predictor=True,
**kwargs,
)
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Raft_Small_Weights.C_T_V2))
def raft_small(*, weights: Optional[Raft_Small_Weights] = None, progress=True, **kwargs) -> RAFT:
"""RAFT "small" model from
`RAFT: Recurrent All Pairs Field Transforms for Optical Flow <https://arxiv.org/abs/2003.12039>`__.
Please see the example below for a tutorial on how to use this model.
Args:
weights(:class:`~torchvision.models.optical_flow.Raft_Small_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.optical_flow.Raft_Small_Weights`
below for more details, and possible values. By default, no
pre-trained weights are used.
progress (bool): If True, displays a progress bar of the download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.optical_flow.RAFT``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/optical_flow/raft.py>`_
for more details about this class.
.. autoclass:: torchvision.models.optical_flow.Raft_Small_Weights
:members:
"""
weights = Raft_Small_Weights.verify(weights)
return _raft(
weights=weights,
progress=progress,
# Feature encoder
feature_encoder_layers=(32, 32, 64, 96, 128),
feature_encoder_block=BottleneckBlock,
feature_encoder_norm_layer=InstanceNorm2d,
# Context encoder
context_encoder_layers=(32, 32, 64, 96, 160),
context_encoder_block=BottleneckBlock,
context_encoder_norm_layer=None,
# Correlation block
corr_block_num_levels=4,
corr_block_radius=3,
# Motion encoder
motion_encoder_corr_layers=(96,),
motion_encoder_flow_layers=(64, 32),
motion_encoder_out_channels=82,
# Recurrent block
recurrent_block_hidden_state_size=96,
recurrent_block_kernel_size=(3,),
recurrent_block_padding=(1,),
# Flow head
flow_head_hidden_size=128,
# Mask predictor
use_mask_predictor=False,
**kwargs,
)
