Source code for gluoncv.model_zoo.monodepthv2.depth_decoder
"""Decoder module of Monodepth2
Code partially borrowed from
https://github.com/nianticlabs/monodepth2/blob/master/networks/depth_decoder.py
"""
# Copyright Niantic 2019. Patent Pending. All rights reserved.
#
# This software is licensed under the terms of the Monodepth2 licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import numpy as np
import mxnet as mx
import mxnet.gluon.nn as nn
from .layers import ConvBlock, Conv3x3
[docs]class DepthDecoder(nn.HybridBlock):
r"""Decoder of Monodepth2
Parameters
----------
num_ch_enc : list
The channels number of encoder.
scales: list
The scales used in the loss. (Default: range(4))
num_output_channels: int
The number of output channels. (Default: 1)
use_skips: bool
This will use skip architecture in the network. (Default: True)
"""
def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1,
use_skips=True):
super(DepthDecoder, self).__init__()
self.num_output_channels = num_output_channels
self.use_skips = use_skips
self.upsample_mode = 'nearest'
self.scales = scales
self.num_ch_enc = num_ch_enc
self.num_ch_dec = np.array([16, 32, 64, 128, 256])
# decoder
with self.name_scope():
self.convs = OrderedDict()
for i in range(4, -1, -1):
# upconv_0
num_ch_in = self.num_ch_enc[-1] if i == 4 else self.num_ch_dec[i + 1]
num_ch_out = self.num_ch_dec[i]
self.convs[("upconv", i, 0)] = ConvBlock(num_ch_in, num_ch_out)
# upconv_1
num_ch_in = self.num_ch_dec[i]
if self.use_skips and i > 0:
num_ch_in += self.num_ch_enc[i - 1]
num_ch_out = self.num_ch_dec[i]
self.convs[("upconv", i, 1)] = ConvBlock(num_ch_in, num_ch_out)
for s in self.scales:
self.convs[("dispconv", s)] = Conv3x3(
self.num_ch_dec[s], self.num_output_channels)
# register blocks
for k in self.convs:
self.register_child(self.convs[k])
self.decoder = nn.HybridSequential()
self.decoder.add(*list(self.convs.values()))
self.sigmoid = nn.Activation('sigmoid')
[docs] def hybrid_forward(self, F, input_features):
# pylint: disable=unused-argument, missing-function-docstring
self.outputs = []
# decoder
x = input_features[-1]
for i in range(4, -1, -1):
x = self.convs[("upconv", i, 0)](x)
x = [F.UpSampling(x, scale=2, sample_type='nearest')]
if self.use_skips and i > 0:
x += [input_features[i - 1]]
x = F.concat(*x, dim=1)
x = self.convs[("upconv", i, 1)](x)
if i in self.scales:
self.outputs.append(self.sigmoid(self.convs[("dispconv", i)](x)))
return self.outputs
def predict(self, input_features):
# pylint: disable=unused-argument, missing-function-docstring
self.outputs = {}
# decoder
x = input_features[-1]
for i in range(4, -1, -1):
x = self.convs[("upconv", i, 0)].predict(x)
x = [mx.nd.UpSampling(x, scale=2, sample_type='nearest')]
if self.use_skips and i > 0:
x += [input_features[i - 1]]
x = mx.nd.concat(*x, dim=1)
x = self.convs[("upconv", i, 1)].predict(x)
if i in self.scales:
self.outputs[("disp", i)] = self.sigmoid(self.convs[("dispconv", i)].predict(x))
return self.outputs