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55 lines
1.8 KiB
Python
55 lines
1.8 KiB
Python
# Copyright Niantic 2019. Patent Pending. All rights reserved.
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#
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# This software is licensed under the terms of the Monodepth2 licence
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# which allows for non-commercial use only, the full terms of which are made
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# available in the LICENSE file.
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from __future__ import absolute_import, division, print_function
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import torch
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import torch.nn as nn
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from collections import OrderedDict
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class PoseDecoder(nn.Module):
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def __init__(self, num_ch_enc, num_input_features, num_frames_to_predict_for=None, stride=1):
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super(PoseDecoder, self).__init__()
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self.num_ch_enc = num_ch_enc
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self.num_input_features = num_input_features
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if num_frames_to_predict_for is None:
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num_frames_to_predict_for = num_input_features - 1
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self.num_frames_to_predict_for = num_frames_to_predict_for
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self.convs = OrderedDict()
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self.convs[("squeeze")] = nn.Conv2d(self.num_ch_enc[-1], 256, 1)
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self.convs[("pose", 0)] = nn.Conv2d(num_input_features * 256, 256, 3, stride, 1)
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self.convs[("pose", 1)] = nn.Conv2d(256, 256, 3, stride, 1)
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self.convs[("pose", 2)] = nn.Conv2d(256, 6 * num_frames_to_predict_for, 1)
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self.relu = nn.ReLU()
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self.net = nn.ModuleList(list(self.convs.values()))
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def forward(self, input_features):
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last_features = [f[-1] for f in input_features]
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cat_features = [self.relu(self.convs["squeeze"](f)) for f in last_features]
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cat_features = torch.cat(cat_features, 1)
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out = cat_features
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for i in range(3):
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out = self.convs[("pose", i)](out)
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if i != 2:
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out = self.relu(out)
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out = out.mean(3).mean(2)
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out = 0.01 * out.view(-1, self.num_frames_to_predict_for, 1, 6)
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axisangle = out[..., :3]
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translation = out[..., 3:]
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return axisangle, translation
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