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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
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