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import torch
import torch.nn as nn
from timm.models.layers import DropPath
from .dvae import Group
from .dvae import Encoder
from .logger import print_log
from collections import OrderedDict
from .checkpoint import get_missing_parameters_message, get_unexpected_parameters_message
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
# NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
self.scale = qk_scale or head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
def forward(self, x):
x = x + self.drop_path(self.attn(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class TransformerEncoder(nn.Module):
""" Transformer Encoder without hierarchical structure
"""
def __init__(self, embed_dim=768, depth=4, num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.):
super().__init__()
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate,
drop_path=drop_path_rate[i] if isinstance(drop_path_rate, list) else drop_path_rate
)
for i in range(depth)])
def forward(self, x, pos):
for _, block in enumerate(self.blocks):
x = block(x + pos)
return x
class PointTransformer(nn.Module):
def __init__(self, config, use_max_pool=True):
super().__init__()
self.config = config
self.use_max_pool = use_max_pool # * whethet to max pool the features of different tokens
self.trans_dim = config.trans_dim
self.depth = config.depth
self.drop_path_rate = config.drop_path_rate
self.cls_dim = config.cls_dim
self.num_heads = config.num_heads
self.group_size = config.group_size
self.num_group = config.num_group
self.point_dims = config.point_dims
# grouper
self.group_divider = Group(num_group=self.num_group, group_size=self.group_size)
# define the encoder
self.encoder_dims = config.encoder_dims
self.encoder = Encoder(encoder_channel=self.encoder_dims, point_input_dims=self.point_dims)
# bridge encoder and transformer
self.reduce_dim = nn.Linear(self.encoder_dims, self.trans_dim)
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.trans_dim))
self.cls_pos = nn.Parameter(torch.randn(1, 1, self.trans_dim))
self.pos_embed = nn.Sequential(
nn.Linear(3, 128),
nn.GELU(),
nn.Linear(128, self.trans_dim)
)
dpr = [x.item() for x in torch.linspace(0, self.drop_path_rate, self.depth)]
self.blocks = TransformerEncoder(
embed_dim=self.trans_dim,
depth=self.depth,
drop_path_rate=dpr,
num_heads=self.num_heads
)
self.norm = nn.LayerNorm(self.trans_dim)
def load_checkpoint(self, bert_ckpt_path):
ckpt = torch.load(bert_ckpt_path, map_location='cpu')
state_dict = OrderedDict()
for k, v in ckpt['state_dict'].items():
if k.startswith('module.point_encoder.'):
state_dict[k.replace('module.point_encoder.', '')] = v
incompatible = self.load_state_dict(state_dict, strict=False)
if incompatible.missing_keys:
print_log('missing_keys', logger='Transformer')
print_log(
get_missing_parameters_message(incompatible.missing_keys),
logger='Transformer'
)
if incompatible.unexpected_keys:
print_log('unexpected_keys', logger='Transformer')
print_log(
get_unexpected_parameters_message(incompatible.unexpected_keys),
logger='Transformer'
)
if not incompatible.missing_keys and not incompatible.unexpected_keys:
# * print successful loading
print_log("PointBERT's weights are successfully loaded from {}".format(bert_ckpt_path), logger='Transformer')
def forward(self, pts):
# divide the point cloud in the same form. This is important
neighborhood, center = self.group_divider(pts)
# encoder the input cloud blocks
group_input_tokens = self.encoder(neighborhood) # B G N
group_input_tokens = self.reduce_dim(group_input_tokens)
# prepare cls
cls_tokens = self.cls_token.expand(group_input_tokens.size(0), -1, -1)
cls_pos = self.cls_pos.expand(group_input_tokens.size(0), -1, -1)
# add pos embedding
pos = self.pos_embed(center)
# final input
x = torch.cat((cls_tokens, group_input_tokens), dim=1)
pos = torch.cat((cls_pos, pos), dim=1)
# transformer
x = self.blocks(x, pos)
x = self.norm(x) # * B, G + 1(cls token)(513), C(384)
if not self.use_max_pool:
return x
concat_f = torch.cat([x[:, 0], x[:, 1:].max(1)[0]], dim=-1).unsqueeze(1) # * concat the cls token and max pool the features of different tokens, make it B, 1, C
return concat_f # * B, 1, C(384 + 384) |