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RNNs.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvGRU(nn.Module):
def __init__(self, hidden_dim=128, input_dim=128*2, kernel_size=(3, 3), padding=(1, 1)):
super(ConvGRU, self).__init__()
self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=padding)
self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=padding)
self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=padding)
def forward(self, h, x):
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz(hx))
r = torch.sigmoid(self.convr(hx))
q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))
h = (1-z) * h + z * q
return h
class CoordEncoder(nn.Module):
def __init__(self, cfg, hidden_dim=128, num_layers=4):
super(CoordEncoder, self).__init__()
self.convs = nn.ModuleList([
nn.Conv2d(2, hidden_dim, kernel_size=(1, 1), padding=0),
])
# (2 * cfg.models.num_encoding_fn_xyz + 1)*2
for idx in range(num_layers):
self.convs.extend([
nn.ReLU(),
nn.Conv2d(hidden_dim, hidden_dim, kernel_size=(1, 1), padding=0),
])
def forward(self, x):
for layer in self.convs:
x = layer(x)
return x
class PoseFeature(nn.Module):
def __init__(self, cfg, input_dim, hidden_dim=128, num_layers=4):
super(PoseFeature, self).__init__()
self.CoordEncoder = CoordEncoder(cfg, hidden_dim, num_layers)
# self.CoordEncoder = self.construct_layers(hidden_dim, num_layers)
self.conv1 = nn.Sequential(
nn.Conv2d(input_dim, hidden_dim, kernel_size=(3, 3), padding=(1, 1)),
nn.ReLU(),
)
self.conv2 = nn.Sequential(
nn.Conv2d(hidden_dim*2, hidden_dim, kernel_size=(3, 3), padding=(1, 1)),
nn.ReLU(),
)
def forward(self, feat, coord):
x = self.conv1(feat)
y = self.CoordEncoder(coord)
z = torch.cat([x, y], dim=1)
z = self.conv2(z)
return z
class GRUPoseRefine(nn.Module):
def __init__(self, cfg, input_dim, hidden_dim=128, num_layers=4):
super(GRUPoseRefine, self).__init__()
self.PoseFeature = PoseFeature(cfg, input_dim, hidden_dim, num_layers)
self.PoseHeader = nn.Sequential(
nn.Linear(hidden_dim, int(hidden_dim//2)),
nn.ReLU(),
nn.Linear(int(hidden_dim//2), 4),
nn.Tanh()
)
self.convGRU = ConvGRU(hidden_dim=hidden_dim, input_dim=input_dim+hidden_dim)
def forward(self, query_feat, pred_feat, pred_grids, h):
pred_grids = F.interpolate(pred_grids.permute(0, 3, 1, 2), size=pred_feat.shape[2:], mode='bilinear')
poseFeat = self.PoseFeature(pred_feat, pred_grids)
x = torch.cat([query_feat, poseFeat], dim=1)
h = self.convGRU(h, x)
h_ = torch.mean(h, dim=[-1, -2])
delta_pose = self.PoseHeader(h_)
return h, delta_pose
class NNrefine(nn.Module):
def __init__(self):
super(NNrefine, self).__init__()
self.linear0 = nn.Sequential(nn.ReLU(inplace=True),
nn.Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))
self.linear1 = nn.Sequential(nn.ReLU(inplace=True),
nn.Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))
self.linear2 = nn.Sequential(nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))
self.linear3 = nn.Sequential(nn.ReLU(inplace=True),
nn.Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))
self.mapping = nn.Sequential(nn.ReLU(inplace=True),
nn.Linear(64, 16),
nn.ReLU(inplace=True),
nn.Linear(16, 3),
nn.Tanh())
def forward(self, pred_feat, ref_feat):
r = pred_feat - ref_feat # [B, C, H, W]
B, C, _, _ = r.shape
if C == 256:
x = self.linear0(r)
elif C == 128:
x = self.linear1(r)
elif C == 64:
x = self.linear2(r)
elif C == 16:
x = self.linear3(r)
x = torch.mean(x, dim=[2, 3])
y = self.mapping(x) # [B, 3]
return y
class Uncertainty(nn.Module):
def __init__(self):
super(Uncertainty, self).__init__()
self.conv1 = nn.Sequential(nn.ReLU(inplace=True),
nn.Conv2d(256, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.Sigmoid())
self.conv2 = nn.Sequential(nn.ReLU(inplace=True),
nn.Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.Sigmoid())
self.conv3 = nn.Sequential(nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(64, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.ReLU(inplace=True),
nn.Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)),
nn.Sigmoid())
def forward(self, x):
return [self.conv1(x[0]), self.conv2(x[1]), self.conv3(x[2])]