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import torch
import torch.nn as nn
import numpy as np
import math
import torch.nn.functional as F
def compute_depth_expectation(prob, depth_values):
depth_values = depth_values.view(*depth_values.shape, 1, 1)
depth = torch.sum(prob * depth_values, 1)
return depth
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
super(ConvBlock, self).__init__()
if kernel_size == 3:
self.conv = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(in_channels, out_channels, 3, padding=0, stride=1),
)
elif kernel_size == 1:
self.conv = nn.Conv2d(int(in_channels), int(out_channels), 1, padding=0, stride=1)
self.nonlin = nn.ELU(inplace=True)
def forward(self, x):
out = self.conv(x)
out = self.nonlin(out)
return out
class ConvBlock_double(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
super(ConvBlock_double, self).__init__()
if kernel_size == 3:
self.conv = nn.Sequential(
nn.ReflectionPad2d(1),
nn.Conv2d(in_channels, out_channels, 3, padding=0, stride=1),
)
elif kernel_size == 1:
self.conv = nn.Conv2d(int(in_channels), int(out_channels), 1, padding=0, stride=1)
self.nonlin = nn.ELU(inplace=True)
self.conv_2 = nn.Conv2d(out_channels, out_channels, 1, padding=0, stride=1)
self.nonlin_2 =nn.ELU(inplace=True)
def forward(self, x):
out = self.conv(x)
out = self.nonlin(out)
out = self.conv_2(out)
out = self.nonlin_2(out)
return out
class DecoderFeature(nn.Module):
def __init__(self, feat_channels, num_ch_dec=[64, 64, 128, 256]):
super(DecoderFeature, self).__init__()
self.num_ch_dec = num_ch_dec
self.feat_channels = feat_channels
self.upconv_3_0 = ConvBlock(self.feat_channels[3], self.num_ch_dec[3], kernel_size=1)
self.upconv_3_1 = ConvBlock_double(
self.feat_channels[2] + self.num_ch_dec[3],
self.num_ch_dec[3],
kernel_size=1)
self.upconv_2_0 = ConvBlock(self.num_ch_dec[3], self.num_ch_dec[2], kernel_size=3)
self.upconv_2_1 = ConvBlock_double(
self.feat_channels[1] + self.num_ch_dec[2],
self.num_ch_dec[2],
kernel_size=3)
self.upconv_1_0 = ConvBlock(self.num_ch_dec[2], self.num_ch_dec[1], kernel_size=3)
self.upconv_1_1 = ConvBlock_double(
self.feat_channels[0] + self.num_ch_dec[1],
self.num_ch_dec[1],
kernel_size=3)
self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
def forward(self, ref_feature):
x = ref_feature[3]
x = self.upconv_3_0(x)
x = torch.cat((self.upsample(x), ref_feature[2]), 1)
x = self.upconv_3_1(x)
x = self.upconv_2_0(x)
x = torch.cat((self.upsample(x), ref_feature[1]), 1)
x = self.upconv_2_1(x)
x = self.upconv_1_0(x)
x = torch.cat((self.upsample(x), ref_feature[0]), 1)
x = self.upconv_1_1(x)
return x
class UNet(nn.Module):
def __init__(self, inp_ch=32, output_chal=1, down_sample_times=3, channel_mode='v0'):
super(UNet, self).__init__()
basic_block = ConvBnReLU
num_depth = 128
self.conv0 = basic_block(inp_ch, num_depth)
if channel_mode == 'v0':
channels = [num_depth, num_depth//2, num_depth//4, num_depth//8, num_depth // 8]
elif channel_mode == 'v1':
channels = [num_depth, num_depth, num_depth, num_depth, num_depth, num_depth]
self.down_sample_times = down_sample_times
for i in range(down_sample_times):
setattr(
self, 'conv_%d' % i,
nn.Sequential(
basic_block(channels[i], channels[i+1], stride=2),
basic_block(channels[i+1], channels[i+1])
)
)
for i in range(down_sample_times-1,-1,-1):
setattr(self, 'deconv_%d' % i,
nn.Sequential(
nn.ConvTranspose2d(
channels[i+1],
channels[i],
kernel_size=3,
padding=1,
output_padding=1,
stride=2,
bias=False),
nn.BatchNorm2d(channels[i]),
nn.ReLU(inplace=True)
)
)
self.prob = nn.Conv2d(num_depth, output_chal, 1, stride=1, padding=0)
def forward(self, x):
features = {}
conv0 = self.conv0(x)
x = conv0
features[0] = conv0
for i in range(self.down_sample_times):
x = getattr(self, 'conv_%d' % i)(x)
features[i+1] = x
for i in range(self.down_sample_times-1,-1,-1):
x = features[i] + getattr(self, 'deconv_%d' % i)(x)
x = self.prob(x)
return x
class ConvBnReLU(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, pad=1):
super(ConvBnReLU, self).__init__()
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride=stride,
padding=pad,
bias=False
)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
return F.relu(self.bn(self.conv(x)), inplace=True)
class HourglassDecoder(nn.Module):
def __init__(self, cfg):
super(HourglassDecoder, self).__init__()
self.inchannels = cfg.model.decode_head.in_channels # [256, 512, 1024, 2048]
self.decoder_channels = cfg.model.decode_head.decoder_channel # [64, 64, 128, 256]
self.min_val = cfg.data_basic.depth_normalize[0]
self.max_val = cfg.data_basic.depth_normalize[1]
self.num_ch_dec = self.decoder_channels # [64, 64, 128, 256]
self.num_depth_regressor_anchor = 512
self.feat_channels = self.inchannels
unet_in_channel = self.num_ch_dec[1]
unet_out_channel = 256
self.decoder_mono = DecoderFeature(self.feat_channels, self.num_ch_dec)
self.conv_out_2 = UNet(inp_ch=unet_in_channel,
output_chal=unet_out_channel + 1,
down_sample_times=3,
channel_mode='v0',
)
self.depth_regressor_2 = nn.Sequential(
nn.Conv2d(unet_out_channel,
self.num_depth_regressor_anchor,
kernel_size=3,
padding=1,
),
nn.BatchNorm2d(self.num_depth_regressor_anchor),
nn.ReLU(inplace=True),
nn.Conv2d(
self.num_depth_regressor_anchor,
self.num_depth_regressor_anchor,
kernel_size=1,
)
)
self.residual_channel = 16
self.conv_up_2 = nn.Sequential(
nn.Conv2d(1 + 2 + unet_out_channel, self.residual_channel, 3, padding=1),
nn.BatchNorm2d(self.residual_channel),
nn.ReLU(),
nn.Conv2d(self.residual_channel, self.residual_channel, 3, padding=1),
nn.Upsample(scale_factor=4),
nn.Conv2d(self.residual_channel, self.residual_channel, 3, padding=1),
nn.ReLU(),
nn.Conv2d(self.residual_channel, 1, 1, padding=0),
)
def get_bins(self, bins_num):
depth_bins_vec = torch.linspace(math.log(self.min_val), math.log(self.max_val), bins_num, device='cuda')
depth_bins_vec = torch.exp(depth_bins_vec)
return depth_bins_vec
def register_depth_expectation_anchor(self, bins_num, B):
depth_bins_vec = self.get_bins(bins_num)
depth_bins_vec = depth_bins_vec.unsqueeze(0).repeat(B, 1)
self.register_buffer('depth_expectation_anchor', depth_bins_vec, persistent=False)
def upsample(self, x, scale_factor=2):
return F.interpolate(x, scale_factor=scale_factor, mode='nearest')
def regress_depth_2(self, feature_map_d):
prob = self.depth_regressor_2(feature_map_d).softmax(dim=1)
B = prob.shape[0]
if "depth_expectation_anchor" not in self._buffers:
self.register_depth_expectation_anchor(self.num_depth_regressor_anchor, B)
d = compute_depth_expectation(
prob,
self.depth_expectation_anchor[:B, ...]
).unsqueeze(1)
return d
def create_mesh_grid(self, height, width, batch, device="cuda", set_buffer=True):
y, x = torch.meshgrid([torch.arange(0, height, dtype=torch.float32, device=device),
torch.arange(0, width, dtype=torch.float32, device=device)], indexing='ij')
meshgrid = torch.stack((x, y))
meshgrid = meshgrid.unsqueeze(0).repeat(batch, 1, 1, 1)
return meshgrid
def forward(self, features_mono, **kwargs):
'''
trans_ref2src: list of transformation matrix from the reference view to source view. [B, 4, 4]
inv_intrinsic_pool: list of inverse intrinsic matrix.
features_mono: features of reference and source views. [[ref_f1, ref_f2, ref_f3, ref_f4],[src1_f1, src1_f2, src1_f3, src1_f4], ...].
'''
outputs = {}
# get encoder feature of the reference view
ref_feat = features_mono
feature_map_mono = self.decoder_mono(ref_feat)
feature_map_mono_pred = self.conv_out_2(feature_map_mono)
confidence_map_2 = feature_map_mono_pred[:, -1:, :, :]
feature_map_d_2 = feature_map_mono_pred[:, :-1, :, :]
depth_pred_2 = self.regress_depth_2(feature_map_d_2)
B, _, H, W = depth_pred_2.shape
meshgrid = self.create_mesh_grid(H, W, B)
depth_pred_mono = self.upsample(depth_pred_2, scale_factor=4) + 1e-1 * \
self.conv_up_2(
torch.cat((depth_pred_2, meshgrid[:B, ...], feature_map_d_2), 1)
)
confidence_map_mono = self.upsample(confidence_map_2, scale_factor=4)
outputs=dict(
prediction=depth_pred_mono,
confidence=confidence_map_mono,
pred_logit=None,
)
return outputs |