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import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from .backbone import CNNEncoder | |
from .transformer import FeatureTransformer, FeatureFlowAttention | |
from .matching import global_correlation_softmax, local_correlation_softmax | |
from .geometry import flow_warp | |
from .utils import normalize_img, feature_add_position | |
class GMFlow(nn.Module): | |
def __init__(self, | |
num_scales=1, | |
upsample_factor=8, | |
feature_channels=128, | |
attention_type='swin', | |
num_transformer_layers=6, | |
ffn_dim_expansion=4, | |
num_head=1, | |
**kwargs, | |
): | |
super(GMFlow, self).__init__() | |
self.num_scales = num_scales | |
self.feature_channels = feature_channels | |
self.upsample_factor = upsample_factor | |
self.attention_type = attention_type | |
self.num_transformer_layers = num_transformer_layers | |
# CNN backbone | |
self.backbone = CNNEncoder(output_dim=feature_channels, num_output_scales=num_scales) | |
# Transformer | |
self.transformer = FeatureTransformer(num_layers=num_transformer_layers, | |
d_model=feature_channels, | |
nhead=num_head, | |
attention_type=attention_type, | |
ffn_dim_expansion=ffn_dim_expansion, | |
) | |
# flow propagation with self-attn | |
self.feature_flow_attn = FeatureFlowAttention(in_channels=feature_channels) | |
# convex upsampling: concat feature0 and flow as input | |
self.upsampler = nn.Sequential(nn.Conv2d(2 + feature_channels, 256, 3, 1, 1), | |
nn.ReLU(inplace=True), | |
nn.Conv2d(256, upsample_factor ** 2 * 9, 1, 1, 0)) | |
def extract_feature(self, img0, img1): | |
concat = torch.cat((img0, img1), dim=0) # [2B, C, H, W] | |
features = self.backbone(concat) # list of [2B, C, H, W], resolution from high to low | |
# reverse: resolution from low to high | |
features = features[::-1] | |
feature0, feature1 = [], [] | |
for i in range(len(features)): | |
feature = features[i] | |
chunks = torch.chunk(feature, 2, 0) # tuple | |
feature0.append(chunks[0]) | |
feature1.append(chunks[1]) | |
return feature0, feature1 | |
def upsample_flow(self, flow, feature, bilinear=False, upsample_factor=8, | |
): | |
if bilinear: | |
up_flow = F.interpolate(flow, scale_factor=upsample_factor, | |
mode='bilinear', align_corners=True) * upsample_factor | |
else: | |
# convex upsampling | |
concat = torch.cat((flow, feature), dim=1) | |
mask = self.upsampler(concat) | |
b, flow_channel, h, w = flow.shape | |
mask = mask.view(b, 1, 9, self.upsample_factor, self.upsample_factor, h, w) # [B, 1, 9, K, K, H, W] | |
mask = torch.softmax(mask, dim=2) | |
up_flow = F.unfold(self.upsample_factor * flow, [3, 3], padding=1) | |
up_flow = up_flow.view(b, flow_channel, 9, 1, 1, h, w) # [B, 2, 9, 1, 1, H, W] | |
up_flow = torch.sum(mask * up_flow, dim=2) # [B, 2, K, K, H, W] | |
up_flow = up_flow.permute(0, 1, 4, 2, 5, 3) # [B, 2, K, H, K, W] | |
up_flow = up_flow.reshape(b, flow_channel, self.upsample_factor * h, | |
self.upsample_factor * w) # [B, 2, K*H, K*W] | |
return up_flow | |
def forward(self, img0, img1, | |
attn_splits_list=None, | |
corr_radius_list=None, | |
prop_radius_list=None, | |
pred_bidir_flow=False, | |
**kwargs, | |
): | |
results_dict = {} | |
flow_preds = [] | |
img0, img1 = normalize_img(img0, img1) # [B, 3, H, W] | |
# resolution low to high | |
feature0_list, feature1_list = self.extract_feature(img0, img1) # list of features | |
flow = None | |
assert len(attn_splits_list) == len(corr_radius_list) == len(prop_radius_list) == self.num_scales | |
for scale_idx in range(self.num_scales): | |
feature0, feature1 = feature0_list[scale_idx], feature1_list[scale_idx] | |
if pred_bidir_flow and scale_idx > 0: | |
# predicting bidirectional flow with refinement | |
feature0, feature1 = torch.cat((feature0, feature1), dim=0), torch.cat((feature1, feature0), dim=0) | |
upsample_factor = self.upsample_factor * (2 ** (self.num_scales - 1 - scale_idx)) | |
if scale_idx > 0: | |
flow = F.interpolate(flow, scale_factor=2, mode='bilinear', align_corners=True) * 2 | |
if flow is not None: | |
flow = flow.detach() | |
feature1 = flow_warp(feature1, flow) # [B, C, H, W] | |
attn_splits = attn_splits_list[scale_idx] | |
corr_radius = corr_radius_list[scale_idx] | |
prop_radius = prop_radius_list[scale_idx] | |
# add position to features | |
feature0, feature1 = feature_add_position(feature0, feature1, attn_splits, self.feature_channels) | |
# Transformer | |
feature0, feature1 = self.transformer(feature0, feature1, attn_num_splits=attn_splits) | |
# correlation and softmax | |
if corr_radius == -1: # global matching | |
flow_pred = global_correlation_softmax(feature0, feature1, pred_bidir_flow)[0] | |
else: # local matching | |
flow_pred = local_correlation_softmax(feature0, feature1, corr_radius)[0] | |
# flow or residual flow | |
flow = flow + flow_pred if flow is not None else flow_pred | |
# upsample to the original resolution for supervison | |
if self.training: # only need to upsample intermediate flow predictions at training time | |
flow_bilinear = self.upsample_flow(flow, None, bilinear=True, upsample_factor=upsample_factor) | |
flow_preds.append(flow_bilinear) | |
# flow propagation with self-attn | |
if pred_bidir_flow and scale_idx == 0: | |
feature0 = torch.cat((feature0, feature1), dim=0) # [2*B, C, H, W] for propagation | |
flow = self.feature_flow_attn(feature0, flow.detach(), | |
local_window_attn=prop_radius > 0, | |
local_window_radius=prop_radius) | |
# bilinear upsampling at training time except the last one | |
if self.training and scale_idx < self.num_scales - 1: | |
flow_up = self.upsample_flow(flow, feature0, bilinear=True, upsample_factor=upsample_factor) | |
flow_preds.append(flow_up) | |
if scale_idx == self.num_scales - 1: | |
flow_up = self.upsample_flow(flow, feature0) | |
flow_preds.append(flow_up) | |
results_dict.update({'flow_preds': flow_preds}) | |
return results_dict | |