third_party/RAFT/core/layer.py

import torch
import torch.nn as nn
import torch.nn.functional as F

import torch
import math
from torch.nn import Module, Dropout

### Gradient Clipping and Zeroing Operations ###

GRAD_CLIP = 0.1

class GradClip(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
        return x

    @staticmethod
    def backward(ctx, grad_x):
        grad_x = torch.where(torch.isnan(grad_x), torch.zeros_like(grad_x), grad_x)
        return grad_x.clamp(min=-0.01, max=0.01)

class GradientClip(nn.Module):
    def __init__(self):
        super(GradientClip, self).__init__()

    def forward(self, x):
        return GradClip.apply(x)

def _make_divisible(v, divisor, min_value=None):
    if min_value is None:
        min_value = divisor
    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
    # Make sure that round down does not go down by more than 10%.
    if new_v < 0.9 * v:
        new_v += divisor
    return new_v

class ConvNextBlock(nn.Module):
    r""" ConvNeXt Block. There are two equivalent implementations:
    (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
    (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
    We use (2) as we find it slightly faster in PyTorch
    
    Args:
        dim (int): Number of input channels.
        drop_path (float): Stochastic depth rate. Default: 0.0
        layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
    """
    def __init__(self, dim, output_dim, layer_scale_init_value=1e-6):
        super().__init__()
        self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
        self.norm = LayerNorm(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, 4 * output_dim) # pointwise/1x1 convs, implemented with linear layers
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(4 * output_dim, dim)
        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)), 
                                    requires_grad=True) if layer_scale_init_value > 0 else None
        self.final = nn.Conv2d(dim, output_dim, kernel_size=1, padding=0)

    def forward(self, x):
        input = x
        x = self.dwconv(x)
        x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.gamma is not None:
            x = self.gamma * x
        x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
        x = self.final(input + x)
        return x
    
class LayerNorm(nn.Module):
    r""" LayerNorm that supports two data formats: channels_last (default) or channels_first. 
    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with 
    shape (batch_size, height, width, channels) while channels_first corresponds to inputs 
    with shape (batch_size, channels, height, width).
    """
    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError 
        self.normalized_shape = (normalized_shape, )
    
    def forward(self, x):
        if self.data_format == "channels_last":
            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x

def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution without padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0)


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1)

class BasicBlock(nn.Module):
    def __init__(self, in_planes, planes, stride=1, norm_layer=nn.BatchNorm2d):
        super().__init__()

        # self.sparse = sparse
        self.conv1 = conv3x3(in_planes, planes, stride)
        self.conv2 = conv3x3(planes, planes)
        self.bn1 = norm_layer(planes)
        self.bn2 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        if stride == 1 and in_planes == planes:
            self.downsample = None
        else:
            self.bn3 = norm_layer(planes)
            self.downsample = nn.Sequential(
                conv1x1(in_planes, planes, stride=stride),
                self.bn3
            )

    def forward(self, x):
        y = x
        y = self.relu(self.bn1(self.conv1(y)))
        y = self.relu(self.bn2(self.conv2(y)))
        if self.downsample is not None:
            x = self.downsample(x)
        return self.relu(x+y)