698 lines
25 KiB
Python
698 lines
25 KiB
Python
#
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#
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# 0=================================0
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# | Kernel Point Convolutions |
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# 0=================================0
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#
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Define network blocks
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#
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Hugues THOMAS - 06/03/2020
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#
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import time
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import math
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import torch
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import torch.nn as nn
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from torch.nn.parameter import Parameter
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from torch.nn.init import kaiming_uniform_
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from kernels.kernel_points import load_kernels
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from utils.ply import write_ply
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Simple functions
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# \**********************/
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#
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def gather(x, idx, method=2):
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"""
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implementation of a custom gather operation for faster backwards.
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:param x: input with shape [N, D_1, ... D_d]
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:param idx: indexing with shape [n_1, ..., n_m]
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:param method: Choice of the method
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:return: x[idx] with shape [n_1, ..., n_m, D_1, ... D_d]
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"""
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if method == 0:
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return x[idx]
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elif method == 1:
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x = x.unsqueeze(1)
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x = x.expand((-1, idx.shape[-1], -1))
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idx = idx.unsqueeze(2)
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idx = idx.expand((-1, -1, x.shape[-1]))
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return x.gather(0, idx)
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elif method == 2:
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for i, ni in enumerate(idx.size()[1:]):
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x = x.unsqueeze(i+1)
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new_s = list(x.size())
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new_s[i+1] = ni
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x = x.expand(new_s)
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n = len(idx.size())
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for i, di in enumerate(x.size()[n:]):
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idx = idx.unsqueeze(i+n)
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new_s = list(idx.size())
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new_s[i+n] = di
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idx = idx.expand(new_s)
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return x.gather(0, idx)
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else:
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raise ValueError('Unkown method')
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def radius_gaussian(sq_r, sig, eps=1e-9):
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"""
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Compute a radius gaussian (gaussian of distance)
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:param sq_r: input radiuses [dn, ..., d1, d0]
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:param sig: extents of gaussians [d1, d0] or [d0] or float
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:return: gaussian of sq_r [dn, ..., d1, d0]
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"""
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return torch.exp(-sq_r / (2 * sig**2 + eps))
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def closest_pool(x, inds):
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"""
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Pools features from the closest neighbors. WARNING: this function assumes the neighbors are ordered.
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:param x: [n1, d] features matrix
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:param inds: [n2, max_num] Only the first column is used for pooling
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:return: [n2, d] pooled features matrix
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"""
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# Add a last row with minimum features for shadow pools
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x = torch.cat((x, torch.zeros_like(x[:1, :])), 0)
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# Get features for each pooling location [n2, d]
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return gather(x, inds[:, 0])
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def max_pool(x, inds):
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"""
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Pools features with the maximum values.
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:param x: [n1, d] features matrix
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:param inds: [n2, max_num] pooling indices
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:return: [n2, d] pooled features matrix
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"""
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# Add a last row with minimum features for shadow pools
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x = torch.cat((x, torch.zeros_like(x[:1, :])), 0)
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# Get all features for each pooling location [n2, max_num, d]
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pool_features = gather(x, inds)
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# Pool the maximum [n2, d]
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max_features, _ = torch.max(pool_features, 1)
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return max_features
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def global_average(x, batch_lengths):
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"""
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Block performing a global average over batch pooling
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:param x: [N, D] input features
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:param batch_lengths: [B] list of batch lengths
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:return: [B, D] averaged features
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"""
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# Loop over the clouds of the batch
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averaged_features = []
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i0 = 0
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for b_i, length in enumerate(batch_lengths):
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# Average features for each batch cloud
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averaged_features.append(torch.mean(x[i0:i0 + length], dim=0))
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# Increment for next cloud
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i0 += length
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# Average features in each batch
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return torch.stack(averaged_features)
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# KPConv class
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# \******************/
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#
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class KPConv(nn.Module):
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def __init__(self, kernel_size, p_dim, in_channels, out_channels, KP_extent, radius,
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fixed_kernel_points='center', KP_influence='linear', aggregation_mode='sum',
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deformable=False, modulated=False):
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"""
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Initialize parameters for KPConvDeformable.
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:param kernel_size: Number of kernel points.
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:param p_dim: dimension of the point space.
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:param in_channels: dimension of input features.
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:param out_channels: dimension of output features.
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:param KP_extent: influence radius of each kernel point.
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:param radius: radius used for kernel point init. Even for deformable, use the config.conv_radius
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:param fixed_kernel_points: fix position of certain kernel points ('none', 'center' or 'verticals').
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:param KP_influence: influence function of the kernel points ('constant', 'linear', 'gaussian').
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:param aggregation_mode: choose to sum influences, or only keep the closest ('closest', 'sum').
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:param deformable: choose deformable or not
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:param modulated: choose if kernel weights are modulated in addition to deformed
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"""
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super(KPConv, self).__init__()
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# Save parameters
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self.K = kernel_size
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self.p_dim = p_dim
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.radius = radius
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self.KP_extent = KP_extent
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self.fixed_kernel_points = fixed_kernel_points
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self.KP_influence = KP_influence
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self.aggregation_mode = aggregation_mode
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self.deformable = deformable
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self.modulated = modulated
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self.in_offset_channels = in_channels
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# Running variable containing deformed KP distance to input points. (used in regularization loss)
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self.min_d2 = None
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self.deformed_KP = None
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self.offset_features = None
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# Initialize weights
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self.weights = Parameter(torch.zeros((self.K, in_channels, out_channels), dtype=torch.float32),
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requires_grad=True)
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# Initiate weights for offsets
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if deformable:
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if modulated:
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self.offset_dim = (self.p_dim + 1) * self.K
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else:
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self.offset_dim = self.p_dim * self.K
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self.offset_conv = KPConv(self.K,
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self.p_dim,
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self.in_offset_channels,
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self.offset_dim,
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KP_extent,
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radius,
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fixed_kernel_points=fixed_kernel_points,
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KP_influence=KP_influence,
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aggregation_mode=aggregation_mode)
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self.offset_bias = Parameter(torch.zeros(self.offset_dim, dtype=torch.float32), requires_grad=True)
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else:
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self.offset_dim = None
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self.offset_conv = None
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self.offset_bias = None
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# Reset parameters
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self.reset_parameters()
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# Initialize kernel points
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self.kernel_points = self.init_KP()
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return
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def reset_parameters(self):
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kaiming_uniform_(self.weights, a=math.sqrt(5))
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if self.deformable:
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nn.init.zeros_(self.offset_bias)
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return
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def init_KP(self):
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"""
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Initialize the kernel point positions in a sphere
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:return: the tensor of kernel points
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"""
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# Create one kernel disposition (as numpy array). Choose the KP distance to center thanks to the KP extent
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K_points_numpy = load_kernels(self.radius,
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self.K,
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dimension=self.p_dim,
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fixed=self.fixed_kernel_points)
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return Parameter(torch.tensor(K_points_numpy, dtype=torch.float32),
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requires_grad=False)
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def forward(self, q_pts, s_pts, neighb_inds, x):
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###################
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# Offset generation
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###################
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if self.deformable:
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# Get offsets with a KPConv that only takes part of the features
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x_offsets = x[:, :self.in_offset_channels]
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self.offset_features = self.offset_conv(q_pts, s_pts, neighb_inds, x_offsets) + self.offset_bias
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if self.modulated:
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# Get offset (in normalized scale) from features
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unscaled_offsets = self.offset_features[:, :self.p_dim * self.K]
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unscaled_offsets = unscaled_offsets.view(-1, self.K, self.p_dim)
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# Get modulations
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modulations = 2 * torch.sigmoid(self.offset_features[:, self.p_dim * self.K:])
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else:
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# Get offset (in normalized scale) from features
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unscaled_offsets = self.offset_features.view(-1, self.K, self.p_dim)
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# No modulations
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modulations = None
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# Rescale offset for this layer
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offsets = unscaled_offsets * self.KP_extent
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else:
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offsets = None
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modulations = None
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######################
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# Deformed convolution
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######################
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# Add a fake point in the last row for shadow neighbors
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s_pts = torch.cat((s_pts, torch.zeros_like(s_pts[:1, :]) + 1e6), 0)
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# Get neighbor points [n_points, n_neighbors, dim]
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neighbors = s_pts[neighb_inds, :]
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# Center every neighborhood
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neighbors = neighbors - q_pts.unsqueeze(1)
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# Apply offsets to kernel points [n_points, n_kpoints, dim]
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if self.deformable:
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self.deformed_KP = offsets + self.kernel_points
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deformed_K_points = self.deformed_KP.unsqueeze(1)
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else:
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deformed_K_points = self.kernel_points
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# Get all difference matrices [n_points, n_neighbors, n_kpoints, dim]
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neighbors.unsqueeze_(2)
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differences = neighbors - deformed_K_points
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# Get the square distances [n_points, n_neighbors, n_kpoints]
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sq_distances = torch.sum(differences ** 2, dim=3)
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# Optimization by ignoring points outside a deformed KP range
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if self.deformable:
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# Save distances for loss
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self.min_d2, _ = torch.min(sq_distances, dim=1)
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# Boolean of the neighbors in range of a kernel point [n_points, n_neighbors]
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in_range = torch.any(sq_distances < self.KP_extent ** 2, dim=2).type(torch.int32)
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# New value of max neighbors
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new_max_neighb = torch.max(torch.sum(in_range, dim=1))
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# For each row of neighbors, indices of the ones that are in range [n_points, new_max_neighb]
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neighb_row_bool, neighb_row_inds = torch.topk(in_range, new_max_neighb.item(), dim=1)
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# Gather new neighbor indices [n_points, new_max_neighb]
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new_neighb_inds = neighb_inds.gather(1, neighb_row_inds, sparse_grad=False)
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# Gather new distances to KP [n_points, new_max_neighb, n_kpoints]
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neighb_row_inds.unsqueeze_(2)
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neighb_row_inds = neighb_row_inds.expand(-1, -1, self.K)
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sq_distances = sq_distances.gather(1, neighb_row_inds, sparse_grad=False)
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# New shadow neighbors have to point to the last shadow point
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new_neighb_inds *= neighb_row_bool
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new_neighb_inds -= (neighb_row_bool.type(torch.int64) - 1) * int(s_pts.shape[0] - 1)
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else:
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new_neighb_inds = neighb_inds
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# Get Kernel point influences [n_points, n_kpoints, n_neighbors]
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if self.KP_influence == 'constant':
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# Every point get an influence of 1.
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all_weights = torch.ones_like(sq_distances)
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all_weights = torch.transpose(all_weights, 1, 2)
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elif self.KP_influence == 'linear':
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# Influence decrease linearly with the distance, and get to zero when d = KP_extent.
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all_weights = torch.clamp(1 - torch.sqrt(sq_distances) / self.KP_extent, min=0.0)
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all_weights = torch.transpose(all_weights, 1, 2)
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elif self.KP_influence == 'gaussian':
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# Influence in gaussian of the distance.
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sigma = self.KP_extent * 0.3
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all_weights = radius_gaussian(sq_distances, sigma)
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all_weights = torch.transpose(all_weights, 1, 2)
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else:
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raise ValueError('Unknown influence function type (config.KP_influence)')
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# In case of closest mode, only the closest KP can influence each point
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if self.aggregation_mode == 'closest':
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neighbors_1nn = torch.argmin(sq_distances, dim=2)
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all_weights *= torch.transpose(nn.functional.one_hot(neighbors_1nn, self.K), 1, 2)
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elif self.aggregation_mode != 'sum':
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raise ValueError("Unknown convolution mode. Should be 'closest' or 'sum'")
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# Add a zero feature for shadow neighbors
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x = torch.cat((x, torch.zeros_like(x[:1, :])), 0)
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# Get the features of each neighborhood [n_points, n_neighbors, in_fdim]
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neighb_x = gather(x, new_neighb_inds)
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# Apply distance weights [n_points, n_kpoints, in_fdim]
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weighted_features = torch.matmul(all_weights, neighb_x)
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# Apply modulations
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if self.deformable and self.modulated:
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weighted_features *= modulations.unsqueeze(2)
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# Apply network weights [n_kpoints, n_points, out_fdim]
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weighted_features = weighted_features.permute((1, 0, 2))
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kernel_outputs = torch.matmul(weighted_features, self.weights)
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# Convolution sum [n_points, out_fdim]
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return torch.sum(kernel_outputs, dim=0)
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def __repr__(self):
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return 'KPConv(radius: {:.2f}, in_feat: {:d}, out_feat: {:d})'.format(self.radius,
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self.in_channels,
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self.out_channels)
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# ----------------------------------------------------------------------------------------------------------------------
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#
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# Complex blocks
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# \********************/
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#
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def block_decider(block_name,
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radius,
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in_dim,
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out_dim,
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layer_ind,
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config):
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if block_name == 'unary':
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return UnaryBlock(in_dim, out_dim, config.use_batch_norm, config.batch_norm_momentum)
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elif block_name in ['simple',
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'simple_deformable',
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'simple_invariant',
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'simple_equivariant',
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'simple_strided',
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'simple_deformable_strided',
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'simple_invariant_strided',
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'simple_equivariant_strided']:
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return SimpleBlock(block_name, in_dim, out_dim, radius, layer_ind, config)
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elif block_name in ['resnetb',
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'resnetb_invariant',
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'resnetb_equivariant',
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'resnetb_deformable',
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'resnetb_strided',
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'resnetb_deformable_strided',
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'resnetb_equivariant_strided',
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'resnetb_invariant_strided']:
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return ResnetBottleneckBlock(block_name, in_dim, out_dim, radius, layer_ind, config)
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elif block_name == 'max_pool' or block_name == 'max_pool_wide':
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return MaxPoolBlock(layer_ind)
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elif block_name == 'global_average':
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return GlobalAverageBlock()
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elif block_name == 'nearest_upsample':
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return NearestUpsampleBlock(layer_ind)
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else:
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raise ValueError('Unknown block name in the architecture definition : ' + block_name)
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class BatchNormBlock(nn.Module):
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def __init__(self, in_dim, use_bn, bn_momentum):
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"""
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Initialize a batch normalization block. If network does not use batch normalization, replace with biases.
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:param in_dim: dimension input features
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:param use_bn: boolean indicating if we use Batch Norm
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:param bn_momentum: Batch norm momentum
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"""
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super(BatchNormBlock, self).__init__()
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self.bn_momentum = bn_momentum
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self.use_bn = use_bn
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self.in_dim = in_dim
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if self.use_bn:
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self.batch_norm = nn.BatchNorm1d(in_dim, momentum=bn_momentum)
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#self.batch_norm = nn.InstanceNorm1d(in_dim, momentum=bn_momentum)
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else:
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self.bias = Parameter(torch.zeros(in_dim, dtype=torch.float32), requires_grad=True)
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return
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def reset_parameters(self):
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nn.init.zeros_(self.bias)
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def forward(self, x):
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if self.use_bn:
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x = x.unsqueeze(2)
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x = x.transpose(0, 2)
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x = self.batch_norm(x)
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x = x.transpose(0, 2)
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return x.squeeze()
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else:
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return x + self.bias
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def __repr__(self):
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return 'BatchNormBlock(in_feat: {:d}, momentum: {:.3f}, only_bias: {:s})'.format(self.in_dim,
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self.bn_momentum,
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str(not self.use_bn))
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class UnaryBlock(nn.Module):
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def __init__(self, in_dim, out_dim, use_bn, bn_momentum, no_relu=False):
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"""
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Initialize a standard unary block with its ReLU and BatchNorm.
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:param in_dim: dimension input features
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:param out_dim: dimension input features
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:param use_bn: boolean indicating if we use Batch Norm
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:param bn_momentum: Batch norm momentum
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"""
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super(UnaryBlock, self).__init__()
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self.bn_momentum = bn_momentum
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self.use_bn = use_bn
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self.no_relu = no_relu
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self.in_dim = in_dim
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self.out_dim = out_dim
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self.mlp = nn.Linear(in_dim, out_dim, bias=False)
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self.batch_norm = BatchNormBlock(out_dim, self.use_bn, self.bn_momentum)
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if not no_relu:
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self.leaky_relu = nn.LeakyReLU(0.1)
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return
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def forward(self, x, batch=None):
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x = self.mlp(x)
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x = self.batch_norm(x)
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if not self.no_relu:
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x = self.leaky_relu(x)
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return x
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def __repr__(self):
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return 'UnaryBlock(in_feat: {:d}, out_feat: {:d}, BN: {:s}, ReLU: {:s})'.format(self.in_dim,
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self.out_dim,
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str(self.use_bn),
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str(not self.no_relu))
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class SimpleBlock(nn.Module):
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def __init__(self, block_name, in_dim, out_dim, radius, layer_ind, config):
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"""
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Initialize a simple convolution block with its ReLU and BatchNorm.
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:param in_dim: dimension input features
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:param out_dim: dimension input features
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:param radius: current radius of convolution
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:param config: parameters
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"""
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super(SimpleBlock, self).__init__()
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# get KP_extent from current radius
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current_extent = radius * config.KP_extent / config.conv_radius
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# Get other parameters
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self.bn_momentum = config.batch_norm_momentum
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self.use_bn = config.use_batch_norm
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self.layer_ind = layer_ind
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self.block_name = block_name
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self.in_dim = in_dim
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self.out_dim = out_dim
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# Define the KPConv class
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self.KPConv = KPConv(config.num_kernel_points,
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config.in_points_dim,
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in_dim,
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out_dim // 2,
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current_extent,
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radius,
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fixed_kernel_points=config.fixed_kernel_points,
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KP_influence=config.KP_influence,
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aggregation_mode=config.aggregation_mode,
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deformable='deform' in block_name,
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modulated=config.modulated)
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# Other opperations
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self.batch_norm = BatchNormBlock(out_dim // 2, self.use_bn, self.bn_momentum)
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self.leaky_relu = nn.LeakyReLU(0.1)
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return
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def forward(self, x, batch):
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|
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if 'strided' in self.block_name:
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q_pts = batch.points[self.layer_ind + 1]
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s_pts = batch.points[self.layer_ind]
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neighb_inds = batch.pools[self.layer_ind]
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else:
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q_pts = batch.points[self.layer_ind]
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s_pts = batch.points[self.layer_ind]
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neighb_inds = batch.neighbors[self.layer_ind]
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|
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x = self.KPConv(q_pts, s_pts, neighb_inds, x)
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return self.leaky_relu(self.batch_norm(x))
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class ResnetBottleneckBlock(nn.Module):
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def __init__(self, block_name, in_dim, out_dim, radius, layer_ind, config):
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|
"""
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|
Initialize a resnet bottleneck block.
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|
:param in_dim: dimension input features
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|
:param out_dim: dimension input features
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|
:param radius: current radius of convolution
|
|
:param config: parameters
|
|
"""
|
|
super(ResnetBottleneckBlock, self).__init__()
|
|
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# get KP_extent from current radius
|
|
current_extent = radius * config.KP_extent / config.conv_radius
|
|
|
|
# Get other parameters
|
|
self.bn_momentum = config.batch_norm_momentum
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|
self.use_bn = config.use_batch_norm
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|
self.block_name = block_name
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|
self.layer_ind = layer_ind
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|
self.in_dim = in_dim
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|
self.out_dim = out_dim
|
|
|
|
# First downscaling mlp
|
|
if in_dim != out_dim // 4:
|
|
self.unary1 = UnaryBlock(in_dim, out_dim // 4, self.use_bn, self.bn_momentum)
|
|
else:
|
|
self.unary1 = nn.Identity()
|
|
|
|
# KPConv block
|
|
self.KPConv = KPConv(config.num_kernel_points,
|
|
config.in_points_dim,
|
|
out_dim // 4,
|
|
out_dim // 4,
|
|
current_extent,
|
|
radius,
|
|
fixed_kernel_points=config.fixed_kernel_points,
|
|
KP_influence=config.KP_influence,
|
|
aggregation_mode=config.aggregation_mode,
|
|
deformable='deform' in block_name,
|
|
modulated=config.modulated)
|
|
self.batch_norm_conv = BatchNormBlock(out_dim // 4, self.use_bn, self.bn_momentum)
|
|
|
|
# Second upscaling mlp
|
|
self.unary2 = UnaryBlock(out_dim // 4, out_dim, self.use_bn, self.bn_momentum, no_relu=True)
|
|
|
|
# Shortcut optional mpl
|
|
if in_dim != out_dim:
|
|
self.unary_shortcut = UnaryBlock(in_dim, out_dim, self.use_bn, self.bn_momentum, no_relu=True)
|
|
else:
|
|
self.unary_shortcut = nn.Identity()
|
|
|
|
# Other operations
|
|
self.leaky_relu = nn.LeakyReLU(0.1)
|
|
|
|
return
|
|
|
|
def forward(self, features, batch):
|
|
|
|
if 'strided' in self.block_name:
|
|
q_pts = batch.points[self.layer_ind + 1]
|
|
s_pts = batch.points[self.layer_ind]
|
|
neighb_inds = batch.pools[self.layer_ind]
|
|
else:
|
|
q_pts = batch.points[self.layer_ind]
|
|
s_pts = batch.points[self.layer_ind]
|
|
neighb_inds = batch.neighbors[self.layer_ind]
|
|
|
|
# First downscaling mlp
|
|
x = self.unary1(features)
|
|
|
|
# Convolution
|
|
x = self.KPConv(q_pts, s_pts, neighb_inds, x)
|
|
x = self.leaky_relu(self.batch_norm_conv(x))
|
|
|
|
# Second upscaling mlp
|
|
x = self.unary2(x)
|
|
|
|
# Shortcut
|
|
if 'strided' in self.block_name:
|
|
shortcut = max_pool(features, neighb_inds)
|
|
else:
|
|
shortcut = features
|
|
shortcut = self.unary_shortcut(shortcut)
|
|
|
|
return self.leaky_relu(x + shortcut)
|
|
|
|
|
|
class GlobalAverageBlock(nn.Module):
|
|
|
|
def __init__(self):
|
|
"""
|
|
Initialize a global average block with its ReLU and BatchNorm.
|
|
"""
|
|
super(GlobalAverageBlock, self).__init__()
|
|
return
|
|
|
|
def forward(self, x, batch):
|
|
return global_average(x, batch.lengths[-1])
|
|
|
|
|
|
class NearestUpsampleBlock(nn.Module):
|
|
|
|
def __init__(self, layer_ind):
|
|
"""
|
|
Initialize a nearest upsampling block with its ReLU and BatchNorm.
|
|
"""
|
|
super(NearestUpsampleBlock, self).__init__()
|
|
self.layer_ind = layer_ind
|
|
return
|
|
|
|
def forward(self, x, batch):
|
|
return closest_pool(x, batch.upsamples[self.layer_ind - 1])
|
|
|
|
def __repr__(self):
|
|
return 'NearestUpsampleBlock(layer: {:d} -> {:d})'.format(self.layer_ind,
|
|
self.layer_ind - 1)
|
|
|
|
|
|
class MaxPoolBlock(nn.Module):
|
|
|
|
def __init__(self, layer_ind):
|
|
"""
|
|
Initialize a max pooling block with its ReLU and BatchNorm.
|
|
"""
|
|
super(MaxPoolBlock, self).__init__()
|
|
self.layer_ind = layer_ind
|
|
return
|
|
|
|
def forward(self, x, batch):
|
|
return max_pool(x, batch.pools[self.layer_ind + 1])
|
|
|