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import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import DropPath
from lavis.models.ulip_models.pointbert.dvae import Group
from lavis.models.ulip_models.pointbert.dvae import Encoder
from lavis.models.ulip_models.pointbert.logger import print_log
from lavis.models.ulip_models.pointbert.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, **kwargs):
super().__init__()
self.config = config
# self.args = kwargs["args"]
self.num_features = 512
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
# 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)
# 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)
# self.load_model_from_ckpt('/export/home/repos/SLIP/pretrained_models/point_transformer_8192.pt')
# if not self.args.evaluate_3d:
## TODO: pass as config
# self.load_model_from_ckpt('/export/home/ULIP/data/initialize_models/ULIP-2_pointbert_last.pt')
# self.cls_head_finetune = nn.Sequential(
# nn.Linear(self.trans_dim * 2, 256),
# nn.ReLU(inplace=True),
# nn.Dropout(0.5),
# nn.Linear(256, self.cls_dim)
# )
# self.build_loss_func()
def build_loss_func(self):
self.loss_ce = nn.CrossEntropyLoss()
def get_loss_acc(self, pred, gt, smoothing=True):
# import pdb; pdb.set_trace()
gt = gt.contiguous().view(-1).long()
if smoothing:
eps = 0.2
n_class = pred.size(1)
one_hot = torch.zeros_like(pred).scatter(1, gt.view(-1, 1), 1)
one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)
log_prb = F.log_softmax(pred, dim=1)
loss = -(one_hot * log_prb).sum(dim=1).mean()
else:
loss = self.loss_ce(pred, gt.long())
pred = pred.argmax(-1)
acc = (pred == gt).sum() / float(gt.size(0))
return loss, acc * 100
def load_model_from_ckpt(self, bert_ckpt_path):
ckpt = torch.load(bert_ckpt_path, map_location='cpu')
base_ckpt = {k.replace("module.", ""): v for k, v in ckpt['state_dict'].items()}
for k in list(base_ckpt.keys()):
if k.startswith('transformer_q') and not k.startswith('transformer_q.cls_head'):
base_ckpt[k[len('transformer_q.'):]] = base_ckpt[k]
elif k.startswith('base_model'):
base_ckpt[k[len('base_model.'):]] = base_ckpt[k]
del base_ckpt[k]
incompatible = self.load_state_dict(base_ckpt, strict=True)
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'
)
print_log(f'[Transformer] Successful Loading the ckpt from {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)
concat_f = torch.cat([x[:, 0], x[:, 1:].max(1)[0]], dim=-1)
# ret = self.cls_head_finetune(concat_f)
return concat_f |