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| # 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 | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from collections import OrderedDict | |
| from layers import * | |
| class DepthDecoder(nn.Module): | |
| def __init__(self, num_ch_enc, scales=range(4), num_output_channels=1, use_skips=True, batch_norm = True): | |
| super(DepthDecoder, self).__init__() | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.num_output_channels = num_output_channels | |
| self.use_skips = use_skips | |
| self.upsample_mode = 'nearest' | |
| self.scales = scales | |
| self.batch_norm = batch_norm | |
| self.num_ch_enc = num_ch_enc | |
| self.num_ch_dec = np.array([16, 32, 64, 128, 256]) | |
| self.convs = OrderedDict() | |
| self.bn = {} | |
| for i in range(4, -1, -1): | |
| self.convs[("deconv", i, 0)] = nn.ConvTranspose2d(self.num_ch_dec[i], self.num_ch_dec[i], 3, stride=2, padding = 1, output_padding = 1) | |
| if self.batch_norm: | |
| self.bn[('bn', i)] = batchNorm(self.num_ch_dec[i]) | |
| # decoder | |
| 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) | |
| self.decoder = nn.ModuleList(list(self.convs.values())) | |
| self.sigmoid = nn.Sigmoid() | |
| def forward(self, input_features): | |
| self.outputs = {} | |
| # decoder | |
| x = input_features[-1] | |
| for i in range(4, -1, -1): | |
| x = self.convs[("upconv", i, 0)](x) | |
| x = [upsample(x)] | |
| # x = [self.convs[("deconv", i, 0)](x)] | |
| if self.use_skips and i > 0: | |
| x += [input_features[i - 1]] | |
| x = torch.cat(x, 1) | |
| x = self.convs[("upconv", i, 1)](x) | |
| if self.batch_norm: | |
| x = self.bn[('bn', i)].to(self.device)(x) | |
| # batchnorm | |
| if i in self.scales: | |
| self.outputs[("disp", i)] = self.sigmoid(self.convs[("dispconv", i)](x)) | |
| return self.outputs | |