emage/skeleton_DME.py

# This script is modified from https://github.com/DeepMotionEditing/deep-motion-editing
# Licensed under:
"""
Copyright (c) 2020, Kfir Aberman, Peizhuo Li, Yijia Weng, Dani Lischinski, Olga Sorkine-Hornung, Daniel Cohen-Or and Baoquan Chen.
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""

import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F


class SkeletonConv(nn.Module):
    def __init__(
        self,
        neighbour_list,
        in_channels,
        out_channels,
        kernel_size,
        joint_num,
        stride=1,
        padding=0,
        bias=True,
        padding_mode="zeros",
        add_offset=False,
        in_offset_channel=0,
    ):
        self.in_channels_per_joint = in_channels // joint_num
        self.out_channels_per_joint = out_channels // joint_num
        if in_channels % joint_num != 0 or out_channels % joint_num != 0:
            raise Exception("BAD")
        super(SkeletonConv, self).__init__()

        if padding_mode == "zeros":
            padding_mode = "constant"
        if padding_mode == "reflection":
            padding_mode = "reflect"

        self.expanded_neighbour_list = []
        self.expanded_neighbour_list_offset = []
        self.neighbour_list = neighbour_list
        self.add_offset = add_offset
        self.joint_num = joint_num

        self.stride = stride
        self.dilation = 1
        self.groups = 1
        self.padding = padding
        self.padding_mode = padding_mode
        self._padding_repeated_twice = (padding, padding)

        for neighbour in neighbour_list:
            expanded = []
            for k in neighbour:
                for i in range(self.in_channels_per_joint):
                    expanded.append(k * self.in_channels_per_joint + i)
            self.expanded_neighbour_list.append(expanded)

        if self.add_offset:
            self.offset_enc = SkeletonLinear(neighbour_list, in_offset_channel * len(neighbour_list), out_channels)

            for neighbour in neighbour_list:
                expanded = []
                for k in neighbour:
                    for i in range(add_offset):
                        expanded.append(k * in_offset_channel + i)
                self.expanded_neighbour_list_offset.append(expanded)

        self.weight = torch.zeros(out_channels, in_channels, kernel_size)
        if bias:
            self.bias = torch.zeros(out_channels)
        else:
            self.register_parameter("bias", None)

        self.mask = torch.zeros_like(self.weight)
        for i, neighbour in enumerate(self.expanded_neighbour_list):
            self.mask[self.out_channels_per_joint * i : self.out_channels_per_joint * (i + 1), neighbour, ...] = 1
        self.mask = nn.Parameter(self.mask, requires_grad=False)

        self.description = (
            "SkeletonConv(in_channels_per_armature={}, out_channels_per_armature={}, kernel_size={}, "
            "joint_num={}, stride={}, padding={}, bias={})".format(
                in_channels // joint_num, out_channels // joint_num, kernel_size, joint_num, stride, padding, bias
            )
        )

        self.reset_parameters()

    def reset_parameters(self):
        for i, neighbour in enumerate(self.expanded_neighbour_list):
            """ Use temporary variable to avoid assign to copy of slice, which might lead to unexpected result """
            tmp = torch.zeros_like(self.weight[self.out_channels_per_joint * i : self.out_channels_per_joint * (i + 1), neighbour, ...])
            nn.init.kaiming_uniform_(tmp, a=math.sqrt(5))
            self.weight[self.out_channels_per_joint * i : self.out_channels_per_joint * (i + 1), neighbour, ...] = tmp
            if self.bias is not None:
                fan_in, _ = nn.init._calculate_fan_in_and_fan_out(
                    self.weight[self.out_channels_per_joint * i : self.out_channels_per_joint * (i + 1), neighbour, ...]
                )
                bound = 1 / math.sqrt(fan_in)
                tmp = torch.zeros_like(self.bias[self.out_channels_per_joint * i : self.out_channels_per_joint * (i + 1)])
                nn.init.uniform_(tmp, -bound, bound)
                self.bias[self.out_channels_per_joint * i : self.out_channels_per_joint * (i + 1)] = tmp

        self.weight = nn.Parameter(self.weight)
        if self.bias is not None:
            self.bias = nn.Parameter(self.bias)

    def set_offset(self, offset):
        if not self.add_offset:
            raise Exception("Wrong Combination of Parameters")
        self.offset = offset.reshape(offset.shape[0], -1)

    def forward(self, input):
        # print('SkeletonConv')
        weight_masked = self.weight * self.mask
        # print(f'input: {input.size()}')
        res = F.conv1d(
            F.pad(input, self._padding_repeated_twice, mode=self.padding_mode),
            weight_masked,
            self.bias,
            self.stride,
            0,
            self.dilation,
            self.groups,
        )

        if self.add_offset:
            offset_res = self.offset_enc(self.offset)
            offset_res = offset_res.reshape(offset_res.shape + (1,))
            res += offset_res / 100
        # print(f'res: {res.size()}')
        return res


class SkeletonLinear(nn.Module):
    def __init__(self, neighbour_list, in_channels, out_channels, extra_dim1=False):
        super(SkeletonLinear, self).__init__()
        self.neighbour_list = neighbour_list
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.in_channels_per_joint = in_channels // len(neighbour_list)
        self.out_channels_per_joint = out_channels // len(neighbour_list)
        self.extra_dim1 = extra_dim1
        self.expanded_neighbour_list = []

        for neighbour in neighbour_list:
            expanded = []
            for k in neighbour:
                for i in range(self.in_channels_per_joint):
                    expanded.append(k * self.in_channels_per_joint + i)
            self.expanded_neighbour_list.append(expanded)

        self.weight = torch.zeros(out_channels, in_channels)
        self.mask = torch.zeros(out_channels, in_channels)
        self.bias = nn.Parameter(torch.Tensor(out_channels))

        self.reset_parameters()

    def reset_parameters(self):
        for i, neighbour in enumerate(self.expanded_neighbour_list):
            tmp = torch.zeros_like(self.weight[i * self.out_channels_per_joint : (i + 1) * self.out_channels_per_joint, neighbour])
            self.mask[i * self.out_channels_per_joint : (i + 1) * self.out_channels_per_joint, neighbour] = 1
            nn.init.kaiming_uniform_(tmp, a=math.sqrt(5))
            self.weight[i * self.out_channels_per_joint : (i + 1) * self.out_channels_per_joint, neighbour] = tmp

        fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
        bound = 1 / math.sqrt(fan_in)
        nn.init.uniform_(self.bias, -bound, bound)

        self.weight = nn.Parameter(self.weight)
        self.mask = nn.Parameter(self.mask, requires_grad=False)

    def forward(self, input):
        input = input.reshape(input.shape[0], -1)
        weight_masked = self.weight * self.mask
        res = F.linear(input, weight_masked, self.bias)
        if self.extra_dim1:
            res = res.reshape(res.shape + (1,))
        return res


class SkeletonPool(nn.Module):
    def __init__(self, edges, pooling_mode, channels_per_edge, last_pool=False):
        super(SkeletonPool, self).__init__()

        if pooling_mode != "mean":
            raise Exception("Unimplemented pooling mode in matrix_implementation")

        self.channels_per_edge = channels_per_edge
        self.pooling_mode = pooling_mode
        self.edge_num = len(edges)
        # self.edge_num = len(edges) + 1
        self.seq_list = []
        self.pooling_list = []
        self.new_edges = []
        degree = [0] * 100  # each element represents the degree of the corresponding joint

        for edge in edges:
            degree[edge[0]] += 1
            degree[edge[1]] += 1

        # seq_list contains multiple sub-lists where each sub-list is an edge chain from the joint whose degree > 2 to the end effectors or joints whose degree > 2.
        def find_seq(j, seq):
            nonlocal self, degree, edges

            if degree[j] > 2 and j != 0:
                self.seq_list.append(seq)
                seq = []

            if degree[j] == 1:
                self.seq_list.append(seq)
                return

            for idx, edge in enumerate(edges):
                if edge[0] == j:
                    find_seq(edge[1], seq + [idx])

        find_seq(0, [])
        # print(f'self.seq_list: {self.seq_list}')

        for seq in self.seq_list:
            if last_pool:
                self.pooling_list.append(seq)
                continue
            if len(seq) % 2 == 1:
                self.pooling_list.append([seq[0]])
                self.new_edges.append(edges[seq[0]])
                seq = seq[1:]
            for i in range(0, len(seq), 2):
                self.pooling_list.append([seq[i], seq[i + 1]])
                self.new_edges.append([edges[seq[i]][0], edges[seq[i + 1]][1]])
        # print(f'self.pooling_list: {self.pooling_list}')
        # print(f'self.new_egdes: {self.new_edges}')

        # add global position
        # self.pooling_list.append([self.edge_num - 1])

        self.description = "SkeletonPool(in_edge_num={}, out_edge_num={})".format(len(edges), len(self.pooling_list))

        self.weight = torch.zeros(len(self.pooling_list) * channels_per_edge, self.edge_num * channels_per_edge)

        for i, pair in enumerate(self.pooling_list):
            for j in pair:
                for c in range(channels_per_edge):
                    self.weight[i * channels_per_edge + c, j * channels_per_edge + c] = 1.0 / len(pair)

        self.weight = nn.Parameter(self.weight, requires_grad=False)

    def forward(self, input: torch.Tensor):
        # print('SkeletonPool')
        # print(f'input: {input.size()}')
        # print(f'self.weight: {self.weight.size()}')
        return torch.matmul(self.weight, input)


class SkeletonUnpool(nn.Module):
    def __init__(self, pooling_list, channels_per_edge):
        super(SkeletonUnpool, self).__init__()
        self.pooling_list = pooling_list
        self.input_edge_num = len(pooling_list)
        self.output_edge_num = 0
        self.channels_per_edge = channels_per_edge
        for t in self.pooling_list:
            self.output_edge_num += len(t)

        self.description = "SkeletonUnpool(in_edge_num={}, out_edge_num={})".format(
            self.input_edge_num,
            self.output_edge_num,
        )

        self.weight = torch.zeros(self.output_edge_num * channels_per_edge, self.input_edge_num * channels_per_edge)

        for i, pair in enumerate(self.pooling_list):
            for j in pair:
                for c in range(channels_per_edge):
                    self.weight[j * channels_per_edge + c, i * channels_per_edge + c] = 1

        self.weight = nn.Parameter(self.weight)
        self.weight.requires_grad_(False)

    def forward(self, input: torch.Tensor):
        # print('SkeletonUnpool')
        # print(f'input: {input.size()}')
        # print(f'self.weight: {self.weight.size()}')
        return torch.matmul(self.weight, input)


"""
Helper functions for skeleton operation
"""


def dfs(x, fa, vis, dist):
    vis[x] = 1
    for y in range(len(fa)):
        if (fa[y] == x or fa[x] == y) and vis[y] == 0:
            dist[y] = dist[x] + 1
            dfs(y, fa, vis, dist)


"""
def find_neighbor_joint(fa, threshold):
    neighbor_list = [[]]
    for x in range(1, len(fa)):
        vis = [0 for _ in range(len(fa))]
        dist = [0 for _ in range(len(fa))]
        dist[0] = 10000
        dfs(x, fa, vis, dist)
        neighbor = []
        for j in range(1, len(fa)):
            if dist[j] <= threshold:
                neighbor.append(j)
        neighbor_list.append(neighbor)

    neighbor = [0]
    for i, x in enumerate(neighbor_list):
        if i == 0: continue
        if 1 in x:
            neighbor.append(i)
            neighbor_list[i] = [0] + neighbor_list[i]
    neighbor_list[0] = neighbor
    return neighbor_list


def build_edge_topology(topology, offset):
    # get all edges (pa, child, offset)
    edges = []
    joint_num = len(topology)
    for i in range(1, joint_num):
        edges.append((topology[i], i, offset[i]))
    return edges
"""


def build_edge_topology(topology):
    # get all edges (pa, child)
    edges = []
    joint_num = len(topology)
    edges.append((0, joint_num))  # add an edge between the root joint and a virtual joint
    for i in range(1, joint_num):
        edges.append((topology[i], i))
    return edges


def build_joint_topology(edges, origin_names):
    parent = []
    offset = []
    names = []
    edge2joint = []
    joint_from_edge = []  # -1 means virtual joint
    joint_cnt = 0
    out_degree = [0] * (len(edges) + 10)
    for edge in edges:
        out_degree[edge[0]] += 1

    # add root joint
    joint_from_edge.append(-1)
    parent.append(0)
    offset.append(np.array([0, 0, 0]))
    names.append(origin_names[0])
    joint_cnt += 1

    def make_topology(edge_idx, pa):
        nonlocal edges, parent, offset, names, edge2joint, joint_from_edge, joint_cnt
        edge = edges[edge_idx]
        if out_degree[edge[0]] > 1:
            parent.append(pa)
            offset.append(np.array([0, 0, 0]))
            names.append(origin_names[edge[1]] + "_virtual")
            edge2joint.append(-1)
            pa = joint_cnt
            joint_cnt += 1

        parent.append(pa)
        offset.append(edge[2])
        names.append(origin_names[edge[1]])
        edge2joint.append(edge_idx)
        pa = joint_cnt
        joint_cnt += 1

        for idx, e in enumerate(edges):
            if e[0] == edge[1]:
                make_topology(idx, pa)

    for idx, e in enumerate(edges):
        if e[0] == 0:
            make_topology(idx, 0)

    return parent, offset, names, edge2joint


def calc_edge_mat(edges):
    edge_num = len(edges)
    # edge_mat[i][j] = distance between edge(i) and edge(j)
    edge_mat = [[100000] * edge_num for _ in range(edge_num)]
    for i in range(edge_num):
        edge_mat[i][i] = 0

    # initialize edge_mat with direct neighbor
    for i, a in enumerate(edges):
        for j, b in enumerate(edges):
            link = 0
            for x in range(2):
                for y in range(2):
                    if a[x] == b[y]:
                        link = 1
            if link:
                edge_mat[i][j] = 1

    # calculate all the pairs distance
    for k in range(edge_num):
        for i in range(edge_num):
            for j in range(edge_num):
                edge_mat[i][j] = min(edge_mat[i][j], edge_mat[i][k] + edge_mat[k][j])
    return edge_mat


def find_neighbor(edges, d):
    """
    Args:
        edges: The list contains N elements, each element represents (parent, child).
        d: Distance between edges (the distance of the same edge is 0 and the distance of adjacent edges is 1).

    Returns:
        The list contains N elements, each element is a list of edge indices whose distance <= d.
    """
    edge_mat = calc_edge_mat(edges)
    neighbor_list = []
    edge_num = len(edge_mat)
    for i in range(edge_num):
        neighbor = []
        for j in range(edge_num):
            if edge_mat[i][j] <= d:
                neighbor.append(j)
        neighbor_list.append(neighbor)

    # # add neighbor for global part
    # global_part_neighbor = neighbor_list[0].copy()
    # """
    # Line #373 is buggy. Thanks @crissallan!!
    # See issue #30 (https://github.com/DeepMotionEditing/deep-motion-editing/issues/30)
    # However, fixing this bug will make it unable to load the pretrained model and
    # affect the reproducibility of quantitative error reported in the paper.
    # It is not a fatal bug so we didn't touch it and we are looking for possible solutions.
    # """
    # for i in global_part_neighbor:
    #     neighbor_list[i].append(edge_num)
    # neighbor_list.append(global_part_neighbor)

    return neighbor_list