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import loguru
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from einops.layers.torch import Rearrange
from einops import rearrange
from .utils import coords_grid, bilinear_sampler, upflow8
from .attention import MultiHeadAttention, LinearPositionEmbeddingSine, ExpPositionEmbeddingSine
from typing import Optional, Tuple
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from .gru import BasicUpdateBlock, GMAUpdateBlock
from .gma import Attention
def initialize_flow(img):
""" Flow is represented as difference between two means flow = mean1 - mean0"""
N, C, H, W = img.shape
mean = coords_grid(N, H, W).to(img.device)
mean_init = coords_grid(N, H, W).to(img.device)
# optical flow computed as difference: flow = mean1 - mean0
return mean, mean_init
class CrossAttentionLayer(nn.Module):
# def __init__(self, dim, cfg, num_heads=8, attn_drop=0., proj_drop=0., drop_path=0., dropout=0.):
def __init__(self, qk_dim, v_dim, query_token_dim, tgt_token_dim, add_flow_token=True, num_heads=8, attn_drop=0., proj_drop=0., drop_path=0., dropout=0., pe='linear'):
super(CrossAttentionLayer, self).__init__()
head_dim = qk_dim // num_heads
self.scale = head_dim ** -0.5
self.query_token_dim = query_token_dim
self.pe = pe
self.norm1 = nn.LayerNorm(query_token_dim)
self.norm2 = nn.LayerNorm(query_token_dim)
self.multi_head_attn = MultiHeadAttention(qk_dim, num_heads)
self.q, self.k, self.v = nn.Linear(query_token_dim, qk_dim, bias=True), nn.Linear(tgt_token_dim, qk_dim, bias=True), nn.Linear(tgt_token_dim, v_dim, bias=True)
self.proj = nn.Linear(v_dim*2, query_token_dim)
self.proj_drop = nn.Dropout(proj_drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.ffn = nn.Sequential(
nn.Linear(query_token_dim, query_token_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(query_token_dim, query_token_dim),
nn.Dropout(dropout)
)
self.add_flow_token = add_flow_token
self.dim = qk_dim
def forward(self, query, key, value, memory, query_coord, patch_size, size_h3w3):
"""
query_coord [B, 2, H1, W1]
"""
B, _, H1, W1 = query_coord.shape
if key is None and value is None:
key = self.k(memory)
value = self.v(memory)
# [B, 2, H1, W1] -> [BH1W1, 1, 2]
query_coord = query_coord.contiguous()
query_coord = query_coord.view(B, 2, -1).permute(0, 2, 1)[:,:,None,:].contiguous().view(B*H1*W1, 1, 2)
if self.pe == 'linear':
query_coord_enc = LinearPositionEmbeddingSine(query_coord, dim=self.dim)
elif self.pe == 'exp':
query_coord_enc = ExpPositionEmbeddingSine(query_coord, dim=self.dim)
short_cut = query
query = self.norm1(query)
if self.add_flow_token:
q = self.q(query+query_coord_enc)
else:
q = self.q(query_coord_enc)
k, v = key, value
x = self.multi_head_attn(q, k, v)
x = self.proj(torch.cat([x, short_cut],dim=2))
x = short_cut + self.proj_drop(x)
x = x + self.drop_path(self.ffn(self.norm2(x)))
return x, k, v
class MemoryDecoderLayer(nn.Module):
def __init__(self, patch_size, query_latent_dim=64, cost_latent_dim=128):
super(MemoryDecoderLayer, self).__init__()
self.patch_size = patch_size # for converting coords into H2', W2' space
self.query_latent_dim = query_latent_dim
query_token_dim, tgt_token_dim = query_latent_dim, cost_latent_dim
qk_dim, v_dim = query_token_dim, query_token_dim
self.cross_attend = CrossAttentionLayer(qk_dim, v_dim, query_token_dim, tgt_token_dim, add_flow_token=True)
def forward(self, query, key, value, memory, coords1, size, size_h3w3):
"""
x: [B*H1*W1, 1, C]
memory: [B*H1*W1, H2'*W2', C]
coords1 [B, 2, H2, W2]
size: B, C, H1, W1
1. Note that here coords0 and coords1 are in H2, W2 space.
Should first convert it into H2', W2' space.
2. We assume the upper-left point to be [0, 0], instead of letting center of upper-left patch to be [0, 0]
"""
x_global, k, v = self.cross_attend(query, key, value, memory, coords1, self.patch_size, size_h3w3)
B, C, H1, W1 = size
C = self.query_latent_dim
x_global = x_global.view(B, H1, W1, C).permute(0, 3, 1, 2)
return x_global, k, v
class MemoryDecoder(nn.Module):
def __init__(self, cfg):
super(MemoryDecoder, self).__init__()
dim = self.dim = cfg.query_latent_dim
self.cfg = cfg
self.flow_token_encoder = nn.Sequential(
nn.Conv2d(81*cfg.cost_heads_num, dim, 1, 1),
nn.GELU(),
nn.Conv2d(dim, dim, 1, 1)
)
self.proj = nn.Conv2d(256, 256, 1)
self.depth = cfg.decoder_depth
self.decoder_layer = MemoryDecoderLayer(dim, cfg)
if self.cfg.gma:
self.update_block = GMAUpdateBlock(self.cfg, hidden_dim=128)
self.att = Attention(args=self.cfg, dim=128, heads=1, max_pos_size=160, dim_head=128)
else:
self.update_block = BasicUpdateBlock(self.cfg, hidden_dim=128)
def upsample_flow(self, flow, mask):
""" Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
N, _, H, W = flow.shape
mask = mask.view(N, 1, 9, 8, 8, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold(8 * flow, [3,3], padding=1)
up_flow = up_flow.view(N, 2, 9, 1, 1, H, W)
up_flow = torch.sum(mask * up_flow, dim=2)
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3)
return up_flow.reshape(N, 2, 8*H, 8*W)
def encode_flow_token(self, cost_maps, coords):
"""
cost_maps - B*H1*W1, cost_heads_num, H2, W2
coords - B, 2, H1, W1
"""
coords = coords.permute(0, 2, 3, 1)
batch, h1, w1, _ = coords.shape
r = 4
dx = torch.linspace(-r, r, 2*r+1)
dy = torch.linspace(-r, r, 2*r+1)
delta = torch.stack(torch.meshgrid(dy, dx), axis=-1).to(coords.device)
centroid = coords.reshape(batch*h1*w1, 1, 1, 2)
delta = delta.view(1, 2*r+1, 2*r+1, 2)
coords = centroid + delta
corr = bilinear_sampler(cost_maps, coords)
corr = corr.view(batch, h1, w1, -1).permute(0, 3, 1, 2)
return corr
def forward(self, cost_memory, context, data={}, flow_init=None):
"""
memory: [B*H1*W1, H2'*W2', C]
context: [B, D, H1, W1]
"""
cost_maps = data['cost_maps']
coords0, coords1 = initialize_flow(context)
if flow_init is not None:
#print("[Using warm start]")
coords1 = coords1 + flow_init
#flow = coords1
flow_predictions = []
context = self.proj(context)
net, inp = torch.split(context, [128, 128], dim=1)
net = torch.tanh(net)
inp = torch.relu(inp)
if self.cfg.gma:
attention = self.att(inp)
size = net.shape
key, value = None, None
for idx in range(self.depth):
coords1 = coords1.detach()
cost_forward = self.encode_flow_token(cost_maps, coords1)
#cost_backward = self.reverse_cost_extractor(cost_maps, coords0, coords1)
query = self.flow_token_encoder(cost_forward)
query = query.permute(0, 2, 3, 1).contiguous().view(size[0]*size[2]*size[3], 1, self.dim)
cost_global, key, value = self.decoder_layer(query, key, value, cost_memory, coords1, size, data['H3W3'])
if self.cfg.only_global:
corr = cost_global
else:
corr = torch.cat([cost_global, cost_forward], dim=1)
flow = coords1 - coords0
if self.cfg.gma:
net, up_mask, delta_flow = self.update_block(net, inp, corr, flow, attention)
else:
net, up_mask, delta_flow = self.update_block(net, inp, corr, flow)
# flow = delta_flow
coords1 = coords1 + delta_flow
flow_up = self.upsample_flow(coords1 - coords0, up_mask)
flow_predictions.append(flow_up)
if self.training:
return flow_predictions
else:
return flow_predictions[-1:]
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