lib/net/BasePIFuNet.py

# -*- coding: utf-8 -*-

# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de

import torch.nn as nn
import pytorch_lightning as pl

from .geometry import index, orthogonal, perspective


class BasePIFuNet(pl.LightningModule):

    def __init__(
            self,
            projection_mode='orthogonal',
            error_term=nn.MSELoss(),
    ):
        """
        :param projection_mode:
        Either orthogonal or perspective.
        It will call the corresponding function for projection.
        :param error_term:
        nn Loss between the predicted [B, Res, N] and the label [B, Res, N]
        """
        super(BasePIFuNet, self).__init__()
        self.name = 'base'

        self.error_term = error_term

        self.index = index
        self.projection = orthogonal if projection_mode == 'orthogonal' else perspective

    def forward(self, points, images, calibs, transforms=None):
        '''
        :param points: [B, 3, N] world space coordinates of points
        :param images: [B, C, H, W] input images
        :param calibs: [B, 3, 4] calibration matrices for each image
        :param transforms: Optional [B, 2, 3] image space coordinate transforms
        :return: [B, Res, N] predictions for each point
        '''
        features = self.filter(images)
        preds = self.query(features, points, calibs, transforms)
        return preds

    def filter(self, images):
        '''
        Filter the input images
        store all intermediate features.
        :param images: [B, C, H, W] input images
        '''
        return None

    def query(self, features, points, calibs, transforms=None):
        '''
        Given 3D points, query the network predictions for each point.
        Image features should be pre-computed before this call.
        store all intermediate features.
        query() function may behave differently during training/testing.
        :param points: [B, 3, N] world space coordinates of points
        :param calibs: [B, 3, 4] calibration matrices for each image
        :param transforms: Optional [B, 2, 3] image space coordinate transforms
        :param labels: Optional [B, Res, N] gt labeling
        :return: [B, Res, N] predictions for each point
        '''
        return None

    def get_error(self, preds, labels):
        '''
        Get the network loss from the last query
        :return: loss term
        '''
        return self.error_term(preds, labels)