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import torch
import torch.nn as nn
from einops.einops import rearrange
from .backbone import ResNet_8_2
from .utils.position_encoding import PositionEncodingSine
from .xoftr_module import LocalFeatureTransformer, FineProcess
class XoFTR_Pretrain(nn.Module):
def __init__(self, config):
super().__init__()
# Misc
self.config = config
self.patch_size = config["pretrain_patch_size"]
# Modules
self.backbone = ResNet_8_2(config['resnet'])
self.pos_encoding = PositionEncodingSine(config['coarse']['d_model'])
self.loftr_coarse = LocalFeatureTransformer(config['coarse'])
self.fine_process = FineProcess(config)
self.mask_token_f = nn.Parameter(torch.zeros(1, config['resnet']["block_dims"][0], 1, 1))
self.mask_token_m = nn.Parameter(torch.zeros(1, config['resnet']["block_dims"][1], 1, 1))
self.mask_token_c = nn.Parameter(torch.zeros(1, config['resnet']["block_dims"][2], 1, 1))
self.out_proj = nn.Linear(config['resnet']["block_dims"][0], 4)
torch.nn.init.normal_(self.mask_token_f, std=.02)
torch.nn.init.normal_(self.mask_token_m, std=.02)
torch.nn.init.normal_(self.mask_token_c, std=.02)
def upsample_mae_mask(self, mae_mask, scale):
assert len(mae_mask.shape) == 2
p = int(mae_mask.shape[1] ** .5)
return mae_mask.reshape(-1, p, p).repeat_interleave(scale, axis=1).repeat_interleave(scale, axis=2)
def upsample_mask(self, mask, scale):
return mask.repeat_interleave(scale, axis=1).repeat_interleave(scale, axis=2)
def mask_layer(self, feat, mae_mask, mae_mask_scale, mask=None, mask_scale=None, mask_token=None):
""" Mask the feature map and replace with trainable inpu tokens if available
Args:
feat (torch.Tensor): [N, C, H, W]
mae_mask (torch.Tensor): (N, L) mask for masked image modeling
mae_mask_scale (int): the scale of layer to mae mask
mask (torch.Generator): mask for padded input image
mask_scale (int): the scale of layer to mask (mask is created on course scale)
mask_token (torch.Tensor): [1, C, 1, 1] learnable mae mask token
Returns:
feat (torch.Tensor): [N, C, H, W]
"""
mae_mask = self.upsample_mae_mask(mae_mask, mae_mask_scale)
mae_mask = mae_mask.unsqueeze(1).type_as(feat)
if mask is not None:
mask = self.upsample_mask(mask, mask_scale)
mask = mask.unsqueeze(1).type_as(feat)
mae_mask = mask * mae_mask
feat = feat * (1. - mae_mask)
if mask_token is not None:
mask_token = mask_token.repeat(feat.shape[0], 1, feat.shape[2], feat.shape[3])
feat += mask_token * mae_mask
return feat
def forward(self, data):
"""
Update:
data (dict): {
'image0': (torch.Tensor): (N, 1, H, W)
'image1': (torch.Tensor): (N, 1, H, W)
'mask0'(optional) : (torch.Tensor): (N, H, W) '0' indicates a padded position
'mask1'(optional) : (torch.Tensor): (N, H, W)
}
"""
# 1. Local Feature CNN
data.update({
'bs': data['image0'].size(0),
'hw0_i': data['image0'].shape[2:], 'hw1_i': data['image1'].shape[2:]
})
image0 = data["image0_norm"] if "image0_norm" in data else data["image0"]
image1 = data["image1_norm"] if "image1_norm" in data else data["image1"]
mask0 = mask1 = None # mask fro madded images
if 'mask0' in data:
mask0, mask1 = data['mask0'], data['mask1']
# mask input images
image0 = self.mask_layer(image0,
data["mae_mask0"],
mae_mask_scale=self.patch_size,
mask=mask0,
mask_scale=8)
image1 = self.mask_layer(image1,
data["mae_mask1"],
mae_mask_scale=self.patch_size,
mask=mask1,
mask_scale=8)
data.update({"masked_image0":image0.clone().detach().cpu(),
"masked_image1":image1.clone().detach().cpu()})
if data['hw0_i'] == data['hw1_i']: # faster & better BN convergence
feats_c, feats_m, feats_f = self.backbone(torch.cat([image0, image1], dim=0))
(feat_c0, feat_c1) = feats_c.split(data['bs'])
(feat_m0, feat_m1) = feats_m.split(data['bs'])
(feat_f0, feat_f1) = feats_f.split(data['bs'])
else: # handle different input shapes
feat_c0, feat_m0, feat_f0 = self.backbone(image0)
feat_c1, feat_m1, feat_f1 = self.backbone(image1)
# mask output layers of backbone and replace with trainable token
feat_c0 = self.mask_layer(feat_c0,
data["mae_mask0"],
mae_mask_scale=self.patch_size // 8,
mask=mask0,
mask_scale=1,
mask_token=self.mask_token_c)
feat_c1 = self.mask_layer(feat_c1,
data["mae_mask1"],
mae_mask_scale=self.patch_size // 8,
mask=mask1,
mask_scale=1,
mask_token=self.mask_token_c)
feat_m0 = self.mask_layer(feat_m0,
data["mae_mask0"],
mae_mask_scale=self.patch_size // 4,
mask=mask0,
mask_scale=2,
mask_token=self.mask_token_m)
feat_m1 = self.mask_layer(feat_m1,
data["mae_mask1"],
mae_mask_scale=self.patch_size // 4,
mask=mask1,
mask_scale=2,
mask_token=self.mask_token_m)
feat_f0 = self.mask_layer(feat_f0,
data["mae_mask0"],
mae_mask_scale=self.patch_size // 2,
mask=mask0,
mask_scale=4,
mask_token=self.mask_token_f)
feat_f1 = self.mask_layer(feat_f1,
data["mae_mask1"],
mae_mask_scale=self.patch_size // 2,
mask=mask1,
mask_scale=4,
mask_token=self.mask_token_f)
data.update({
'hw0_c': feat_c0.shape[2:], 'hw1_c': feat_c1.shape[2:],
'hw0_m': feat_m0.shape[2:], 'hw1_m': feat_m1.shape[2:],
'hw0_f': feat_f0.shape[2:], 'hw1_f': feat_f1.shape[2:]
})
# save coarse features for fine matching module
feat_c0_pre, feat_c1_pre = feat_c0.clone(), feat_c1.clone()
# 2. Coarse-level loftr module
# add featmap with positional encoding, then flatten it to sequence [N, HW, C]
feat_c0 = rearrange(self.pos_encoding(feat_c0), 'n c h w -> n (h w) c')
feat_c1 = rearrange(self.pos_encoding(feat_c1), 'n c h w -> n (h w) c')
mask_c0 = mask_c1 = None # mask is useful in training
if 'mask0' in data:
mask_c0, mask_c1 = data['mask0'].flatten(-2), data['mask1'].flatten(-2)
feat_c0, feat_c1 = self.loftr_coarse(feat_c0, feat_c1, mask_c0, mask_c1)
# 3. Fine-level maching module as decoder
# generate window locations from mae mask to reconstruct
mae_mask_c0 = self.upsample_mae_mask( data["mae_mask0"],
self.patch_size // 8)
if mask0 is not None:
mae_mask_c0 = mae_mask_c0 * mask0.type_as(mae_mask_c0)
mae_mask_c1 = self.upsample_mae_mask( data["mae_mask1"],
self.patch_size // 8)
if mask1 is not None:
mae_mask_c1 = mae_mask_c1 * mask1.type_as(mae_mask_c1)
mae_mask_c = torch.logical_or(mae_mask_c0, mae_mask_c1)
b_ids, i_ids = mae_mask_c.flatten(1).nonzero(as_tuple=True)
j_ids = i_ids
# b_ids, i_ids and j_ids are masked location for both images
# ids_image0 and ids_image1 determines which indeces belogs to which image
ids_image0 = mae_mask_c0.flatten(1)[b_ids, i_ids]
ids_image1 = mae_mask_c1.flatten(1)[b_ids, j_ids]
data.update({'b_ids': b_ids, 'i_ids': i_ids, 'j_ids': j_ids,
'ids_image0': ids_image0==1, 'ids_image1': ids_image1==1})
# fine level matching module
feat_f0_unfold, feat_f1_unfold = self.fine_process( feat_f0, feat_f1,
feat_m0, feat_m1,
feat_c0, feat_c1,
feat_c0_pre, feat_c1_pre,
data)
# output projection 5x5 window to 10x10 window
pred0 = self.out_proj(feat_f0_unfold)
pred1 = self.out_proj(feat_f1_unfold)
data.update({"pred0":pred0, "pred1": pred1})
def load_state_dict(self, state_dict, *args, **kwargs):
for k in list(state_dict.keys()):
if k.startswith('matcher.'):
state_dict[k.replace('matcher.', '', 1)] = state_dict.pop(k)
return super().load_state_dict(state_dict, *args, **kwargs)
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