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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 torch | |
| import torch.nn as nn | |
| from collections import OrderedDict | |
| import pdb | |
| import torch.nn.functional as F | |
| # from options import MonodepthOptions | |
| # options = MonodepthOptions() | |
| # opts = options.parse() | |
| class PoseDecoder(nn.Module): | |
| def __init__(self, num_ch_enc, num_input_features, num_frames_to_predict_for=None, stride=1): | |
| super(PoseDecoder, self).__init__() | |
| self.num_ch_enc = num_ch_enc | |
| self.num_input_features = num_input_features | |
| if num_frames_to_predict_for is None: | |
| num_frames_to_predict_for = num_input_features - 1 | |
| self.num_frames_to_predict_for = num_frames_to_predict_for | |
| self.convs = OrderedDict() | |
| self.convs[("squeeze")] = nn.Conv2d(self.num_ch_enc[-1], 256, 1) | |
| self.convs[("pose", 0)] = nn.Conv2d(num_input_features * 256, 256, 3, stride, 1) | |
| self.convs[("pose", 1)] = nn.Conv2d(256, 256, 3, stride, 1) | |
| self.convs[("pose", 2)] = nn.Conv2d(256, 6 * num_frames_to_predict_for, 1) | |
| self.convs[("intrinsics", 'focal')] = nn.Conv2d(256, 2, kernel_size = 3,stride = 1,padding = 1) | |
| self.convs[("intrinsics", 'offset')] = nn.Conv2d(256, 2, kernel_size = 3,stride = 1,padding = 1) | |
| self.relu = nn.ReLU() | |
| self.net = nn.ModuleList(list(self.convs.values())) | |
| def forward(self, input_features): | |
| last_features = [f[-1] for f in input_features] | |
| cat_features = [self.relu(self.convs["squeeze"](f)) for f in last_features] | |
| cat_features = torch.cat(cat_features, 1) | |
| feat = cat_features | |
| for i in range(2): | |
| feat = self.convs[("pose", i)](feat) | |
| feat = self.relu(feat) | |
| out = self.convs[("pose", 2)](feat) | |
| out = out.mean(3).mean(2) | |
| out = 0.01 * out.view(-1, self.num_frames_to_predict_for, 1, 6) | |
| axisangle = out[..., :3] | |
| translation = out[..., 3:] | |
| #add_intrinsics_head | |
| scales = torch.tensor([256,256]).cuda() | |
| focals = F.softplus(self.convs[("intrinsics", 'focal')](feat)).mean(3).mean(2)*scales | |
| offset = (F.softplus(self.convs[("intrinsics", 'offset')](feat)).mean(3).mean(2)+0.5)*scales | |
| #focals = F.softplus(self.convs[("intrinsics",'focal')](feat).mean(3).mean(2)) | |
| #offset = F.softplus(self.convs[("intrinsics",'offset')](feat).mean(3).mean(2)) | |
| eyes = torch.eye(2).cuda() | |
| b,xy = focals.shape | |
| focals = focals.unsqueeze(-1).expand(b,xy,xy) | |
| eyes = eyes.unsqueeze(0).expand(b,xy,xy) | |
| intrin = focals*eyes | |
| offset = offset.view(b,2,1).contiguous() | |
| intrin = torch.cat([intrin,offset],-1) | |
| pad = torch.tensor([0.0,0.0,1.0]).view(1,1,3).expand(b,1,3).cuda() | |
| intrinsics = torch.cat([intrin,pad],1) | |
| return axisangle, translation,intrinsics | |