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'''
* Copyright (c) 2023, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Le Xue
'''
## FROM: https://github.com/salesforce/ULIP
## TODO: Convert to LAVIS format. Currently only supports functionality for XInstructBLIP
# Modified from github.com/openai/CLIP
from collections import OrderedDict
import timm
from torch import nn
from lavis.models.ulip_models import losses
from torch.nn.parameter import Parameter
from easydict import EasyDict
import torch
import numpy as np
from lavis.common.dist_utils import download_cached_file
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
super().__init__()
self.attn = nn.MultiheadAttention(d_model, n_head)
self.ln_1 = LayerNorm(d_model)
self.mlp = nn.Sequential(OrderedDict([
("c_fc", nn.Linear(d_model, d_model * 4)),
("gelu", QuickGELU()),
("c_proj", nn.Linear(d_model * 4, d_model))
]))
self.ln_2 = LayerNorm(d_model)
self.attn_mask = attn_mask
def attention(self, x: torch.Tensor):
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
def forward(self, x: torch.Tensor):
x = x + self.attention(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class Transformer(nn.Module):
def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
super().__init__()
self.width = width
self.layers = layers
self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
def forward(self, x: torch.Tensor):
return self.resblocks(x)
class ULIP_WITH_IMAGE(nn.Module):
def __init__(self, point_encoder, **kwargs):
# super().__init__(ssl_mlp_dim, ssl_emb_dim, **kwargs)
super().__init__()
kwargs = EasyDict(kwargs)
self.context_length = kwargs.context_length
self.vision_width = kwargs.vision_width
self.visual = kwargs.vision_model
self.num_features = kwargs.embed_dim
self.transformer = Transformer(
width=kwargs.transformer_width,
layers=kwargs.transformer_layers,
heads=kwargs.transformer_heads,
attn_mask=self.build_attention_mask(),
)
self.vocab_size = kwargs.vocab_size
self.token_embedding = nn.Embedding(kwargs.vocab_size, kwargs.transformer_width)
self.positional_embedding = nn.Parameter(torch.empty(self.context_length, kwargs.transformer_width))
self.ln_final = LayerNorm(kwargs.transformer_width)
self.image_projection = nn.Parameter(torch.empty(kwargs.vision_width, kwargs.embed_dim))
self.text_projection = nn.Parameter(torch.empty(kwargs.transformer_width, kwargs.embed_dim))
self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
self.initialize_parameters()
self.point_encoder = point_encoder
self.pc_projection = nn.Parameter(torch.empty(kwargs.pc_feat_dims, kwargs.embed_dim ))
nn.init.normal_(self.pc_projection, std= kwargs.embed_dim ** -0.5)
def encode_image(self, image):
x = self.visual(image)
x = x @ self.image_projection
return x
def encode_text(self, text):
x = self.token_embedding(text) # [batch_size, n_ctx, d_model]
x = x + self.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_final(x)
# take features from the eot embedding (eot_token is the highest number in each sequence)
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
return x
def build_attention_mask(self):
# lazily create causal attention mask, with full attention between the vision tokens
# pytorch uses additive attention mask; fill with -inf
mask = torch.empty(self.context_length, self.context_length)
mask.fill_(float("-inf"))
mask.triu_(1) # zero out the lower diagonal
return mask
def initialize_parameters(self):
nn.init.normal_(self.token_embedding.weight, std=0.02)
nn.init.normal_(self.positional_embedding, std=0.01)
proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
attn_std = self.transformer.width ** -0.5
fc_std = (2 * self.transformer.width) ** -0.5
for block in self.transformer.resblocks:
nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
nn.init.normal_(self.image_projection, std=self.vision_width ** -0.5)
nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)
def encode_pc(self, pc):
pc_feat = self.point_encoder(pc)
pc_embed = pc_feat @ self.pc_projection
return pc_embed
def forward(self, pc, text=None, image=None):
if text is not None:
text_embed_all = []
for i in range(text.shape[0]):
text_for_one_sample = text[i]
text_embed = self.encode_text(text_for_one_sample)
text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
text_embed = text_embed.mean(dim=0)
text_embed = text_embed / text_embed.norm(dim=-1, keepdim=True)
text_embed_all.append(text_embed)
text_embed_all = torch.stack(text_embed_all)
else:
text_embed_all = None
pc_embed = self.encode_pc(pc)
if image is not None:
image_embed = self.encode_image(image)
else:
image_embed = None
res = {'text_embed': text_embed_all,
'pc_embed': pc_embed,
'image_embed': image_embed,
'logit_scale': self.logit_scale.exp()
}
return pc_embed
def get_loss(args):
return losses.ULIPWithImageLoss()
def get_metric_names(model):
return ['loss', 'ulip_loss', 'ulip_pc_image_acc', 'ulip_pc_text_acc']
def ULIP_PointBERT(ulip_v=2):
vision_model = timm.create_model('vit_base_patch16_224', num_classes=0)
# =====================================================================
# import the 3D backbone and specify the output point cloud feature dimension
from lavis.models.ulip_models.pointbert.point_encoder import PointTransformer
from lavis.models.ulip_models.utils.config import cfg_from_yaml_file
## TODO: parse as config
# config_addr = '/export/home/LAVIS/lavis/models/ulip_models/pointbert/PointTransformer_8192point.yaml'
url = "https://raw.githubusercontent.com/salesforce/ULIP/48d8d00b1cdb2aee79005817a202816f1c521911/models/pointbert/PointTransformer_8192point.yaml"
config_addr = download_cached_file(
url, check_hash=False, progress=True
)
config = cfg_from_yaml_file(config_addr)
pc_feat_dims = 768
if ulip_v == "ulip2_scaledup":
config.model.depth = 18
transformer_layers = 18
embed_dim=1280
else:
embed_dim=512
transformer_layers = 12
point_encoder = PointTransformer(config.model)
# =====================================================================
model = ULIP_WITH_IMAGE(embed_dim=embed_dim, vision_width=pc_feat_dims, point_encoder=point_encoder, vision_model=vision_model,
context_length=77, vocab_size=49408,
transformer_width=512, transformer_heads=8, transformer_layers=transformer_layers, pc_feat_dims=pc_feat_dims)
## TODO: setup config
if ulip_v == 2:
cached_file = '/export/share/lxue/shared_models/ULIP-2/objaverse_shapenet_k_5/ULIP-2_pointbert_last.pt'
elif ulip_v == 1:
cached_file = '/export/share/lxue/shared_models/ULIP-1/objaverse/ULIP-1_pointbert_last.pt'
elif ulip_v == 'shapenet':
cached_file = '/export/share/lxue/shared_models/ULIP-1/objaverse_shapenet/checkpoint_last.pt'
elif ulip_v == 'objaverse_k_1':
cached_file = '/export/share/lxue/shared_models/ULIP-2/objaverse_k_1/checkpoint_last.pt'
elif ulip_v == 'objaverse_shapenet_k_1':
cached_file = '/export/share/lxue/shared_models/ULIP-2/objaverse_shapenet_k_1/checkpoint_last.pt'
elif ulip_v == "ulip2_scaledup":
cached_file = "/export/share/lxue/shared_models/ULIP-2/objaverse_shapenet_k_1_scaled_up/checkpoint_last.pt"
# url = "https://storage.cloud.google.com/sfr-ulip-code-release-research/pretrained_models/ckpt_zero-sho_classification/checkpoint_pointbert.pt"
# cached_file = download_cached_file(
# url, check_hash=False, progress=True
# )
ckpt = torch.load(cached_file, map_location='cpu')
state_dict = OrderedDict()
for k, v in ckpt['state_dict'].items():
state_dict[k.replace('module.', '')] = v
# model.cuda()
model.load_state_dict(state_dict, strict=False)
return model |