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"""MonoDepthNet: Network for monocular depth estimation trained by mixing several datasets.
This file contains code that is adapted from
https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
"""
import torch
import torch.nn as nn
from torchvision import models
class MonoDepthNet(nn.Module):
"""Network for monocular depth estimation.
"""
def __init__(self, path=None, features=256):
"""Init.
Args:
path (str, optional): Path to saved model. Defaults to None.
features (int, optional): Number of features. Defaults to 256.
"""
super().__init__()
resnet = models.resnet50(pretrained=False)
self.pretrained = nn.Module()
self.scratch = nn.Module()
self.pretrained.layer1 = nn.Sequential(resnet.conv1, resnet.bn1, resnet.relu,
resnet.maxpool, resnet.layer1)
self.pretrained.layer2 = resnet.layer2
self.pretrained.layer3 = resnet.layer3
self.pretrained.layer4 = resnet.layer4
# adjust channel number of feature maps
self.scratch.layer1_rn = nn.Conv2d(256, features, kernel_size=3, stride=1, padding=1, bias=False)
self.scratch.layer2_rn = nn.Conv2d(512, features, kernel_size=3, stride=1, padding=1, bias=False)
self.scratch.layer3_rn = nn.Conv2d(1024, features, kernel_size=3, stride=1, padding=1, bias=False)
self.scratch.layer4_rn = nn.Conv2d(2048, features, kernel_size=3, stride=1, padding=1, bias=False)
self.scratch.refinenet4 = FeatureFusionBlock(features)
self.scratch.refinenet3 = FeatureFusionBlock(features)
self.scratch.refinenet2 = FeatureFusionBlock(features)
self.scratch.refinenet1 = FeatureFusionBlock(features)
# adaptive output module: 2 convolutions and upsampling
self.scratch.output_conv = nn.Sequential(nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
nn.Conv2d(128, 1, kernel_size=3, stride=1, padding=1),
Interpolate(scale_factor=2, mode='bilinear'))
# load model
if path:
self.load(path)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input data (image)
Returns:
tensor: depth
"""
layer_1 = self.pretrained.layer1(x)
layer_2 = self.pretrained.layer2(layer_1)
layer_3 = self.pretrained.layer3(layer_2)
layer_4 = self.pretrained.layer4(layer_3)
layer_1_rn = self.scratch.layer1_rn(layer_1)
layer_2_rn = self.scratch.layer2_rn(layer_2)
layer_3_rn = self.scratch.layer3_rn(layer_3)
layer_4_rn = self.scratch.layer4_rn(layer_4)
path_4 = self.scratch.refinenet4(layer_4_rn)
path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
out = self.scratch.output_conv(path_1)
return out
def load(self, path):
"""Load model from file.
Args:
path (str): file path
"""
parameters = torch.load(path)
self.load_state_dict(parameters)
class Interpolate(nn.Module):
"""Interpolation module.
"""
def __init__(self, scale_factor, mode):
"""Init.
Args:
scale_factor (float): scaling
mode (str): interpolation mode
"""
super(Interpolate, self).__init__()
self.interp = nn.functional.interpolate
self.scale_factor = scale_factor
self.mode = mode
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: interpolated data
"""
x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=False)
return x
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True)
self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.relu(x)
out = self.conv1(out)
out = self.relu(out)
out = self.conv2(out)
return out + x
class FeatureFusionBlock(nn.Module):
"""Feature fusion block.
"""
def __init__(self, features):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.resConfUnit = ResidualConvUnit(features)
def forward(self, *xs):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
output += self.resConfUnit(xs[1])
output = self.resConfUnit(output)
output = nn.functional.interpolate(output, scale_factor=2,
mode='bilinear', align_corners=True)
return output
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