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from typing import Any, Callable, Iterable, List, Optional, Union
import torch
import torch.jit as jit
import torch.nn as nn
import torch.nn.functional as F
from torch import Size, Tensor, nn
from torch.nn import LayerNorm
from model.pvcnn.pvcnn_utils import get_timestep_embedding
def sample_b(size: Size, sigma: float) -> Tensor:
"""Sample b matrix for fourier features
Arguments:
size (Size): b matrix size
sigma (float): std of the gaussian
Returns:
b (Tensor): b matrix
"""
return torch.randn(size) * sigma
@jit.script
def map_positional_encoding(v: Tensor, freq_bands: Tensor) -> Tensor:
"""Map v to positional encoding representation phi(v)
Arguments:
v (Tensor): input features (B, IFeatures)
freq_bands (Tensor): frequency bands (N_freqs, )
Returns:
phi(v) (Tensor): fourrier features (B, 3 + (2 * N_freqs) * 3)
"""
pe = [v]
for freq in freq_bands:
fv = freq * v
pe += [torch.sin(fv), torch.cos(fv)]
return torch.cat(pe, dim=-1)
@jit.script
def map_fourier_features(v: Tensor, b: Tensor) -> Tensor:
"""Map v to fourier features representation phi(v)
Arguments:
v (Tensor): input features (B, IFeatures)
b (Tensor): b matrix (OFeatures, IFeatures)
Returns:
phi(v) (Tensor): fourrier features (B, 2 * Features)
"""
PI = 3.141592653589793
a = 2 * PI * v @ b.T
return torch.cat((torch.sin(a), torch.cos(a)), dim=-1)
class FeatureMapping(nn.Module):
"""FeatureMapping nn.Module
Maps v to features following transformation phi(v)
Arguments:
i_dim (int): input dimensions
o_dim (int): output dimensions
"""
def __init__(self, i_dim: int, o_dim: int) -> None:
super().__init__()
self.i_dim = i_dim
self.o_dim = o_dim
def forward(self, v: Tensor) -> Tensor:
"""FeratureMapping forward pass
Arguments:
v (Tensor): input features (B, IFeatures)
Returns:
phi(v) (Tensor): mapped features (B, OFeatures)
"""
raise NotImplementedError("Forward pass not implemented yet!")
class PositionalEncoding(FeatureMapping):
"""PositionalEncoding module
Maps v to positional encoding representation phi(v)
Arguments:
i_dim (int): input dimension for v
N_freqs (int): #frequency to sample (default: 10)
"""
def __init__(
self,
i_dim: int,
N_freqs: int = 10,
) -> None:
super().__init__(i_dim, 3 + (2 * N_freqs) * 3)
self.N_freqs = N_freqs
a, b = 1, self.N_freqs - 1
freq_bands = 2 ** torch.linspace(a, b, self.N_freqs)
self.register_buffer("freq_bands", freq_bands)
def forward(self, v: Tensor) -> Tensor:
"""Map v to positional encoding representation phi(v)
Arguments:
v (Tensor): input features (B, IFeatures)
Returns:
phi(v) (Tensor): fourrier features (B, 3 + (2 * N_freqs) * 3)
"""
return map_positional_encoding(v, self.freq_bands)
class FourierFeatures(FeatureMapping):
"""Fourier Features module
Maps v to fourier features representation phi(v)
Arguments:
i_dim (int): input dimension for v
features (int): output dimension (default: 256)
sigma (float): std of the gaussian (default: 26.)
"""
def __init__(
self,
i_dim: int,
features: int = 256,
sigma: float = 26.,
) -> None:
super().__init__(i_dim, 2 * features)
self.features = features
self.sigma = sigma
self.size = Size((self.features, self.i_dim))
self.register_buffer("b", sample_b(self.size, self.sigma))
def forward(self, v: Tensor) -> Tensor:
"""Map v to fourier features representation phi(v)
Arguments:
v (Tensor): input features (B, IFeatures)
Returns:
phi(v) (Tensor): fourrier features (B, 2 * Features)
"""
return map_fourier_features(v, self.b)
class FeedForward(nn.Module):
""" Adapted from the FeedForward layer from labmlai """
def __init__(
self,
d_in: int,
d_hidden: int,
d_out: int,
activation: Callable = nn.ReLU(),
is_gated: bool = False,
bias1: bool = True,
bias2: bool = True,
bias_gate: bool = True,
dropout: float = 0.1,
use_layernorm: bool = False,
):
super().__init__()
# Layer one parameterized by weight $W_1$ and bias $b_1$
self.layer1 = nn.Linear(d_in, d_hidden, bias=bias1)
# Layer one parameterized by weight $W_1$ and bias $b_1$
self.layer2 = nn.Linear(d_hidden, d_out, bias=bias2)
# Hidden layer dropout
self.dropout = nn.Dropout(dropout)
# Activation function $f$
self.activation = activation
# Whether there is a gate
self.is_gated = is_gated
if is_gated:
# If there is a gate the linear layer to transform inputs to
# be multiplied by the gate, parameterized by weight $V$ and bias $c$
self.linear_v = nn.Linear(d_in, d_hidden, bias=bias_gate)
# Whether to add a layernorm layer
self.use_layernorm = use_layernorm
if use_layernorm:
self.layernorm = LayerNorm(d_in)
def forward(self, x: Tensor, coords: Tensor = None) -> Tensor:
"""Applies a simple feed forward layer"""
x = self.layernorm(x) if self.use_layernorm else x
g = self.activation(self.layer1(x))
x = (g * self.linear_v(x)) if self.is_gated else g
x = self.dropout(x)
x = self.layer2(x)
return x
class BasePointModel(nn.Module):
""" A base class providing useful methods for point cloud processing. """
def __init__(
self,
*,
num_classes,
embed_dim,
extra_feature_channels,
dim: int = 128,
num_layers: int = 6
):
super().__init__()
self.extra_feature_channels = extra_feature_channels
self.timestep_embed_dim = embed_dim
self.output_dim = num_classes
self.dim = dim
self.num_layers = num_layers
# Time embedding function
self.timestep_projection = nn.Sequential(
nn.Linear(embed_dim, embed_dim),
nn.LeakyReLU(0.1, inplace=True),
nn.Linear(embed_dim, embed_dim),
)
# Positional encoding
self.positional_encoding = PositionalEncoding(i_dim=3, N_freqs=10)
positional_encoding_d_out = 3 + (2 * 10) * 3
# Input projection (point coords, point coord encodings, other features, and timestep embeddings)
self.input_projection = nn.Linear(
in_features=(3 + positional_encoding_d_out + extra_feature_channels + self.timestep_embed_dim),
out_features=self.dim
)
# Transformer layers
self.layers = self.get_layers()
# Output projection
self.output_projection = nn.Linear(self.dim, self.output_dim)
def get_layers(self):
raise NotImplementedError('This method should be implemented by subclasses')
def prepare_inputs(self, inputs: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
"""
The inputs have size (B, 3 + S, N), where S is the number of additional
feature channels and N is the number of points. The timesteps t can be either
continuous or discrete. This model has a sort of U-Net-like structure I think,
which is why it first goes down and then up in terms of resolution (?)
"""
# Embed and project timesteps
t_emb = get_timestep_embedding(self.timestep_embed_dim, t, inputs.device)
t_emb = self.timestep_projection(t_emb)[:, None, :].expand(-1, inputs.shape[-1], -1) # (B, N, D_t_emb)
# Separate input coordinates and features
x = torch.transpose(inputs, -2, -1) # -> (B, N, 3 + S)
coords = x[:, :, :3] # (B, N, 3), point coordinates
# Positional encoding of point coords
coords_posenc = self.positional_encoding(coords) # (B, N, D_p_enc)
# Project
x = torch.cat((x, coords_posenc, t_emb), dim=2) # (B, N, 3 + S + D_p_enc + D_t_emb)
x = self.input_projection(x) # (B, N, D_model)
return x, coords
def get_global_tensors(self, x: Tensor):
B, N, D = x.shape
x_pool_max = torch.max(x, dim=1, keepdim=True).values.repeat(1, N, 1) # (B, 1, D)
x_pool_std = torch.std(x, dim=1, keepdim=True).repeat(1, N, 1) # (B, 1, D)
return x_pool_max, x_pool_std
def forward(self, inputs: torch.Tensor, t: torch.Tensor):
raise NotImplementedError('This method should be implemented by subclasses') |