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Running
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Zero
File size: 9,209 Bytes
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import torch
import torch.nn as nn
import torch.nn.functional as F
from .update import BasicUpdateBlock
from .extractor import BasicEncoder
from .corr import AGCL
from .attention import PositionEncodingSine, LocalFeatureTransformer
try:
autocast = torch.cuda.amp.autocast
except:
# dummy autocast for PyTorch < 1.6
class autocast:
def __init__(self, enabled):
pass
def __enter__(self):
pass
def __exit__(self, *args):
pass
#Ref: https://github.com/princeton-vl/RAFT/blob/master/core/raft.py
class CREStereo(nn.Module):
def __init__(self, max_disp=192, mixed_precision=False, test_mode=False):
super(CREStereo, self).__init__()
self.max_flow = max_disp
self.mixed_precision = mixed_precision
self.test_mode = test_mode
self.hidden_dim = 128
self.context_dim = 128
self.dropout = 0
self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=self.dropout)
self.update_block = BasicUpdateBlock(hidden_dim=self.hidden_dim, cor_planes=4 * 9, mask_size=4)
# loftr
self.self_att_fn = LocalFeatureTransformer(
d_model=256, nhead=8, layer_names=["self"] * 1, attention="linear"
)
self.cross_att_fn = LocalFeatureTransformer(
d_model=256, nhead=8, layer_names=["cross"] * 1, attention="linear"
)
# adaptive search
self.search_num = 9
self.conv_offset_16 = nn.Conv2d(
256, self.search_num * 2, kernel_size=3, stride=1, padding=1
)
self.conv_offset_8 = nn.Conv2d(
256, self.search_num * 2, kernel_size=3, stride=1, padding=1
)
self.range_16 = 1
self.range_8 = 1
def freeze_bn(self):
for m in self.modules():
if isinstance(m, nn.BatchNorm2d):
m.eval()
def convex_upsample(self, flow, mask, rate=4):
""" Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """
N, _, H, W = flow.shape
# print(flow.shape, mask.shape, rate)
mask = mask.view(N, 1, 9, rate, rate, H, W)
mask = torch.softmax(mask, dim=2)
up_flow = F.unfold(rate * 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, rate*H, rate*W)
def zero_init(self, fmap):
N, C, H, W = fmap.shape
_x = torch.zeros([N, 1, H, W], dtype=torch.float32)
_y = torch.zeros([N, 1, H, W], dtype=torch.float32)
zero_flow = torch.cat((_x, _y), dim=1).to(fmap.device)
return zero_flow
def forward(self, image1, image2, flow_init=None, iters=10, upsample=True, test_mode=False):
""" Estimate optical flow between pair of frames """
image1 = 2 * (image1 / 255.0) - 1.0
image2 = 2 * (image2 / 255.0) - 1.0
image1 = image1.contiguous()
image2 = image2.contiguous()
hdim = self.hidden_dim
cdim = self.context_dim
# run the feature network
with autocast(enabled=self.mixed_precision):
fmap1, fmap2 = self.fnet([image1, image2])
fmap1 = fmap1.float()
fmap2 = fmap2.float()
with autocast(enabled=self.mixed_precision):
# 1/4 -> 1/8
# feature
fmap1_dw8 = F.avg_pool2d(fmap1, 2, stride=2)
fmap2_dw8 = F.avg_pool2d(fmap2, 2, stride=2)
# offset
offset_dw8 = self.conv_offset_8(fmap1_dw8)
offset_dw8 = self.range_8 * (torch.sigmoid(offset_dw8) - 0.5) * 2.0
# context
net, inp = torch.split(fmap1, [hdim,hdim], dim=1)
net = torch.tanh(net)
inp = F.relu(inp)
net_dw8 = F.avg_pool2d(net, 2, stride=2)
inp_dw8 = F.avg_pool2d(inp, 2, stride=2)
# 1/4 -> 1/16
# feature
fmap1_dw16 = F.avg_pool2d(fmap1, 4, stride=4)
fmap2_dw16 = F.avg_pool2d(fmap2, 4, stride=4)
offset_dw16 = self.conv_offset_16(fmap1_dw16)
offset_dw16 = self.range_16 * (torch.sigmoid(offset_dw16) - 0.5) * 2.0
# context
net_dw16 = F.avg_pool2d(net, 4, stride=4)
inp_dw16 = F.avg_pool2d(inp, 4, stride=4)
# positional encoding and self-attention
pos_encoding_fn_small = PositionEncodingSine(
d_model=256, max_shape=(image1.shape[2] // 16, image1.shape[3] // 16)
)
# 'n c h w -> n (h w) c'
x_tmp = pos_encoding_fn_small(fmap1_dw16)
fmap1_dw16 = x_tmp.permute(0, 2, 3, 1).reshape(x_tmp.shape[0], x_tmp.shape[2] * x_tmp.shape[3], x_tmp.shape[1])
# 'n c h w -> n (h w) c'
x_tmp = pos_encoding_fn_small(fmap2_dw16)
fmap2_dw16 = x_tmp.permute(0, 2, 3, 1).reshape(x_tmp.shape[0], x_tmp.shape[2] * x_tmp.shape[3], x_tmp.shape[1])
fmap1_dw16, fmap2_dw16 = self.self_att_fn(fmap1_dw16, fmap2_dw16)
fmap1_dw16, fmap2_dw16 = [
x.reshape(x.shape[0], image1.shape[2] // 16, -1, x.shape[2]).permute(0, 3, 1, 2)
for x in [fmap1_dw16, fmap2_dw16]
]
corr_fn = AGCL(fmap1, fmap2)
corr_fn_dw8 = AGCL(fmap1_dw8, fmap2_dw8)
corr_fn_att_dw16 = AGCL(fmap1_dw16, fmap2_dw16, att=self.cross_att_fn)
# Cascaded refinement (1/16 + 1/8 + 1/4)
predictions = []
flow = None
flow_up = None
if flow_init is not None:
scale = fmap1.shape[2] / flow_init.shape[2]
flow = -scale * F.interpolate(
flow_init,
size=(fmap1.shape[2], fmap1.shape[3]),
mode="bilinear",
align_corners=True,
)
else:
# zero initialization
flow_dw16 = self.zero_init(fmap1_dw16)
# Recurrent Update Module
# RUM: 1/16
for itr in range(iters // 2):
if itr % 2 == 0:
small_patch = False
else:
small_patch = True
flow_dw16 = flow_dw16.detach()
out_corrs = corr_fn_att_dw16(
flow_dw16, offset_dw16, small_patch=small_patch
)
with autocast(enabled=self.mixed_precision):
net_dw16, up_mask, delta_flow = self.update_block(
net_dw16, inp_dw16, out_corrs, flow_dw16
)
flow_dw16 = flow_dw16 + delta_flow
flow = self.convex_upsample(flow_dw16, up_mask, rate=4)
flow_up = -4 * F.interpolate(
flow,
size=(4 * flow.shape[2], 4 * flow.shape[3]),
mode="bilinear",
align_corners=True,
)
predictions.append(flow_up)
scale = fmap1_dw8.shape[2] / flow.shape[2]
flow_dw8 = -scale * F.interpolate(
flow,
size=(fmap1_dw8.shape[2], fmap1_dw8.shape[3]),
mode="bilinear",
align_corners=True,
)
# RUM: 1/8
for itr in range(iters // 2):
if itr % 2 == 0:
small_patch = False
else:
small_patch = True
flow_dw8 = flow_dw8.detach()
out_corrs = corr_fn_dw8(flow_dw8, offset_dw8, small_patch=small_patch)
with autocast(enabled=self.mixed_precision):
net_dw8, up_mask, delta_flow = self.update_block(
net_dw8, inp_dw8, out_corrs, flow_dw8
)
flow_dw8 = flow_dw8 + delta_flow
flow = self.convex_upsample(flow_dw8, up_mask, rate=4)
flow_up = -2 * F.interpolate(
flow,
size=(2 * flow.shape[2], 2 * flow.shape[3]),
mode="bilinear",
align_corners=True,
)
predictions.append(flow_up)
scale = fmap1.shape[2] / flow.shape[2]
flow = -scale * F.interpolate(
flow,
size=(fmap1.shape[2], fmap1.shape[3]),
mode="bilinear",
align_corners=True,
)
# RUM: 1/4
for itr in range(iters):
if itr % 2 == 0:
small_patch = False
else:
small_patch = True
flow = flow.detach()
out_corrs = corr_fn(flow, None, small_patch=small_patch, iter_mode=True)
with autocast(enabled=self.mixed_precision):
net, up_mask, delta_flow = self.update_block(net, inp, out_corrs, flow)
flow = flow + delta_flow
flow_up = -self.convex_upsample(flow, up_mask, rate=4)
predictions.append(flow_up)
if self.test_mode:
return flow_up
return predictions
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