ldm/modules/encoders/modules.py

import torch 
import torch.nn as nn 
from functools import partial 
from ldm.modules.x_transformer import Encoder, TransformerWrapper # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test 
from torch.utils.checkpoint import checkpoint 
from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel, AutoTokenizer 
from importlib_resources import files 
from ldm.modules.encoders.CLAP.utils import read_config_as_args 
from ldm.modules.encoders.CLAP.clap import TextEncoder 
import copy 
from ldm.util import default, count_params 
import pytorch_lightning as pl 
 
class AbstractEncoder(pl.LightningModule): 
 def __init__(self): 
 super().__init__() 
 
 def encode(self, *args, **kwargs): 
 raise NotImplementedError 
 
 
class ClassEmbedder(nn.Module): 
 def __init__(self, embed_dim, n_classes=1000, key='class'): 
 super().__init__() 
 self.key = key 
 self.embedding = nn.Embedding(n_classes, embed_dim) 
 
 def forward(self, batch, key=None): 
 if key is None: 
 key = self.key 
 # this is for use in crossattn 
 c = batch[key][:, None]# (bsz,1) 
 c = self.embedding(c) 
 return c 
 
 
class TransformerEmbedder(AbstractEncoder): 
 """Some transformer encoder layers""" 
 def __init__(self, n_embed, n_layer, vocab_size, max_seq_len=77, device="cuda"): 
 super().__init__() 
 self.device = device 
 self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, 
 attn_layers=Encoder(dim=n_embed, depth=n_layer)) 
 
 def forward(self, tokens): 
 tokens = tokens.to(self.device) # meh 
 z = self.transformer(tokens, return_embeddings=True) 
 return z 
 
 def encode(self, x): 
 return self(x) 
 
 
class BERTTokenizer(AbstractEncoder): 
 """ Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)""" 
 def __init__(self, device="cuda", vq_interface=True, max_length=77): 
 super().__init__() 
 from transformers import BertTokenizerFast # TODO: add to reuquirements 
 self.tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased") 
 self.device = device 
 self.vq_interface = vq_interface 
 self.max_length = max_length 
 
 def forward(self, text): 
 batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 tokens = batch_encoding["input_ids"].to(self.device) 
 return tokens 
 
 @torch.no_grad() 
 def encode(self, text): 
 tokens = self(text) 
 if not self.vq_interface: 
 return tokens 
 return None, None, [None, None, tokens] 
 
 def decode(self, text): 
 return text 
 
 
class BERTEmbedder(AbstractEncoder):# 这里不是用的pretrained bert,是用的transformers的BertTokenizer加自定义的TransformerWrapper 
 """Uses the BERT tokenizr model and add some transformer encoder layers""" 
 def __init__(self, n_embed, n_layer, vocab_size=30522, max_seq_len=77, 
 device="cuda",use_tokenizer=True, embedding_dropout=0.0): 
 super().__init__() 
 self.use_tknz_fn = use_tokenizer 
 if self.use_tknz_fn: 
 self.tknz_fn = BERTTokenizer(vq_interface=False, max_length=max_seq_len) 
 self.device = device 
 self.transformer = TransformerWrapper(num_tokens=vocab_size, max_seq_len=max_seq_len, 
 attn_layers=Encoder(dim=n_embed, depth=n_layer), 
 emb_dropout=embedding_dropout) 
 
 def forward(self, text): 
 if self.use_tknz_fn: 
 tokens = self.tknz_fn(text)#.to(self.device) 
 else: 
 tokens = text 
 z = self.transformer(tokens, return_embeddings=True) 
 return z 
 
 def encode(self, text): 
 # output of length 77 
 return self(text) 
 
 
class SpatialRescaler(nn.Module): 
 def __init__(self, 
 n_stages=1, 
 method='bilinear', 
 multiplier=0.5, 
 in_channels=3, 
 out_channels=None, 
 bias=False): 
 super().__init__() 
 self.n_stages = n_stages 
 assert self.n_stages >= 0 
 assert method in ['nearest','linear','bilinear','trilinear','bicubic','area'] 
 self.multiplier = multiplier 
 self.interpolator = partial(torch.nn.functional.interpolate, mode=method) 
 self.remap_output = out_channels is not None 
 if self.remap_output: 
 print(f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.') 
 self.channel_mapper = nn.Conv2d(in_channels,out_channels,1,bias=bias) 
 
 def forward(self,x): 
 for stage in range(self.n_stages): 
 x = self.interpolator(x, scale_factor=self.multiplier) 
 
 
 if self.remap_output: 
 x = self.channel_mapper(x) 
 return x 
 
 def encode(self, x): 
 return self(x) 
 
def disabled_train(self, mode=True): 
 """Overwrite model.train with this function to make sure train/eval mode 
 does not change anymore.""" 
 return self 
 
class FrozenT5Embedder(AbstractEncoder): 
 """Uses the T5 transformer encoder for text""" 
 def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl 
 super().__init__() 
 self.tokenizer = T5Tokenizer.from_pretrained(version) 
 self.transformer = T5EncoderModel.from_pretrained(version) 
 self.device = device 
 self.max_length = max_length # TODO: typical value? 
 if freeze: 
 self.freeze() 
 
 def freeze(self): 
 self.transformer = self.transformer.eval() 
 #self.train = disabled_train 
 for param in self.parameters(): 
 param.requires_grad = False 
 
 def forward(self, text): 
 batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 tokens = batch_encoding["input_ids"].to(self.device) 
 outputs = self.transformer(input_ids=tokens) 
 
 z = outputs.last_hidden_state 
 return z 
 
 def encode(self, text): 
 return self(text) 
 
class FrozenFLANEmbedder(AbstractEncoder): 
 """Uses the T5 transformer encoder for text""" 
 def __init__(self, version="google/flan-t5-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl 
 super().__init__() 
 self.tokenizer = T5Tokenizer.from_pretrained(version) 
 self.transformer = T5EncoderModel.from_pretrained(version) 
 self.device = device 
 self.max_length = max_length # TODO: typical value? 
 if freeze: 
 self.freeze() 
 
 def freeze(self): 
 self.transformer = self.transformer.eval() 
 #self.train = disabled_train 
 for param in self.parameters(): 
 param.requires_grad = False 
 
 def forward(self, text): 
 batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 tokens = batch_encoding["input_ids"].to(self.device)# tango的flanT5是不定长度的batch，这里做成定长的batch 
 outputs = self.transformer(input_ids=tokens) 
 
 z = outputs.last_hidden_state 
 return z 
 
 def encode(self, text): 
 return self(text) 
 
class FrozenCLAPEmbedder(AbstractEncoder): 
 """Uses the CLAP transformer encoder for text from microsoft""" 
 def __init__(self, weights_path, freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 
 super().__init__() 
 
 model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model'] 
 match_params = dict() 
 for key in list(model_state_dict.keys()): 
 if 'caption_encoder' in key: 
 match_params[key.replace('caption_encoder.', '')] = model_state_dict[key] 
 
 config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() 
 args = read_config_as_args(config_as_str, is_config_str=True) 
 
 # To device 
 self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model 
 self.caption_encoder = TextEncoder( 
 args.d_proj, args.text_model, args.transformer_embed_dim 
 ) 
 
 self.max_length = max_length 
 self.device = device 
 if freeze: self.freeze() 
 
 print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") 
 
 def freeze(self):# only freeze 
 self.caption_encoder.base = self.caption_encoder.base.eval() 
 for param in self.caption_encoder.base.parameters(): 
 param.requires_grad = False 
 
 
 def encode(self, text): 
 batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 tokens = batch_encoding["input_ids"].to(self.device) 
 
 outputs = self.caption_encoder.base(input_ids=tokens) 
 z = self.caption_encoder.projection(outputs.last_hidden_state) 
 return z 
 
class FrozenLAIONCLAPEmbedder(AbstractEncoder): 
 """Uses the CLAP transformer encoder for text from LAION-AI""" 
 def __init__(self, weights_path, freeze=True,sentence=False, device="cuda", max_length=77): # clip-vit-base-patch32 
 super().__init__() 
 # To device 
 from transformers import RobertaTokenizer 
 from ldm.modules.encoders.open_clap import create_model 
 self.sentence = sentence 
 
 model, model_cfg = create_model( 
 'HTSAT-tiny', 
 'roberta', 
 weights_path, 
 enable_fusion=True, 
 fusion_type='aff_2d' 
 ) 
 
 del model.audio_branch, model.audio_transform, model.audio_projection 
 self.tokenizer = RobertaTokenizer.from_pretrained('roberta-base') 
 self.model = model 
 
 self.max_length = max_length 
 self.device = device 
 self.to(self.device) 
 if freeze: self.freeze() 
 
 param_num = sum(p.numel() for p in model.parameters()) 
 print(f'{self.model.__class__.__name__} comes with: {param_num / 1e6:.3f} M params.') 
 
 def to(self,device): 
 self.model.to(device=device) 
 self.device=device 
 
 def freeze(self): 
 self.model = self.model.eval() 
 for param in self.model.parameters(): 
 param.requires_grad = False 
 
 def encode(self, text): 
 batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, padding="max_length", return_tensors="pt").to(self.device) 
 if self.sentence: 
 z = self.model.get_text_embedding(batch_encoding).unsqueeze(1) 
 else: 
 # text_branch is roberta 
 outputs = self.model.text_branch(input_ids=batch_encoding["input_ids"].to(self.device), attention_mask=batch_encoding["attention_mask"].to(self.device)) 
 z = self.model.text_projection(outputs.last_hidden_state) 
 
 return z 
 
class FrozenLAIONCLAPSetenceEmbedder(AbstractEncoder): 
 """Uses the CLAP transformer encoder for text from LAION-AI""" 
 def __init__(self, weights_path, freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 
 super().__init__() 
 # To device 
 from transformers import RobertaTokenizer 
 from ldm.modules.encoders.open_clap import create_model 
 
 
 model, model_cfg = create_model( 
 'HTSAT-tiny', 
 'roberta', 
 weights_path, 
 enable_fusion=True, 
 fusion_type='aff_2d' 
 ) 
 
 del model.audio_branch, model.audio_transform, model.audio_projection 
 self.tokenize = RobertaTokenizer.from_pretrained('roberta-base') 
 self.model = model 
 
 self.max_length = max_length 
 self.device = device 
 if freeze: self.freeze() 
 
 param_num = sum(p.numel() for p in model.parameters()) 
 print(f'{self.model.__class__.__name__} comes with: {param_num / 1e+6:.3f} M params.') 
 
 def freeze(self): 
 self.model = self.model.eval() 
 for param in self.model.parameters(): 
 param.requires_grad = False 
 
 def tokenizer(self, text): 
 result = self.tokenize( 
 text, 
 padding="max_length", 
 truncation=True, 
 max_length=512, 
 return_tensors="pt", 
 ) 
 return result 
 
 def encode(self, text): 
 with torch.no_grad(): 
 # the 'fusion' truncate mode can be changed to 'rand_trunc' if run in unfusion mode 
 text_data = self.tokenizer(text)# input_ids shape:(b,512) 
 embed = self.model.get_text_embedding(text_data) 
 embed = embed.unsqueeze(1)# (b,1,512) 
 return embed 
 
class FrozenCLAPOrderEmbedder2(AbstractEncoder):# 每个object后面都加上| 
 """Uses the CLAP transformer encoder for text (from huggingface)""" 
 def __init__(self, weights_path, freeze=True, device="cuda"): 
 super().__init__() 
 
 model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model'] 
 match_params = dict() 
 for key in list(model_state_dict.keys()): 
 if 'caption_encoder' in key: 
 match_params[key.replace('caption_encoder.', '')] = model_state_dict[key] 
 
 config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() 
 args = read_config_as_args(config_as_str, is_config_str=True) 
 
 # To device 
 self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model 
 self.caption_encoder = TextEncoder( 
 args.d_proj, args.text_model, args.transformer_embed_dim 
 ).to(device) 
 self.max_objs = 10 
 self.max_length = args.text_len 
 self.device = device 
 self.order_to_label = self.build_order_dict() 
 if freeze: self.freeze() 
 
 print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") 
 
 def freeze(self): 
 self.caption_encoder.base = self.caption_encoder.base.eval() 
 for param in self.caption_encoder.base.parameters(): 
 param.requires_grad = False 
 
 def build_order_dict(self): 
 order2label = {} 
 num_orders = 10 
 time_stamps = ['start','mid','end'] 
 time_num = len(time_stamps) 
 for i in range(num_orders): 
 for j,time_stamp in enumerate(time_stamps): 
 order2label[f'order {i} {time_stamp}'] = i * time_num + j 
 order2label['all'] = num_orders*len(time_stamps) 
 order2label['unknown'] = num_orders*len(time_stamps) + 1 
 return order2label 
 
 def encode(self, text): 
 obj_list,orders_list = [],[] 
 for raw in text: 
 splits = raw.split('@') # raw example: '<man speaking& order 1 start>@<man speaking& order 2 mid>@<idle engine& all>' 
 objs = [] 
 orders = [] 
 for split in splits:# <obj& order> 
 split = split[1:-1] 
 obj,order = split.split('&') 
 objs.append(obj.strip()) 
 try: 
 orders.append(self.order_to_label[order.strip()]) 
 except: 
 print(order.strip(),raw) 
 assert len(objs) == len(orders) 
 obj_list.append(' | '.join(objs)+' |')# '|' after every word 
 orders_list.append(orders) 
 batch_encoding = self.tokenizer(obj_list, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 tokens = batch_encoding["input_ids"] 
 
 outputs = self.caption_encoder.base(input_ids=tokens.to(self.device)) 
 z = self.caption_encoder.projection(outputs.last_hidden_state) 
 return {'token_embedding':z,'token_ids':tokens,'orders':orders_list} 
 
class FrozenCLAPOrderEmbedder3(AbstractEncoder):# 相比于FrozenCLAPOrderEmbedder2移除了projection,使用正确的max_len,去除了order仅保留时间。 
 """Uses the CLAP transformer encoder for text (from huggingface)""" 
 def __init__(self, weights_path, freeze=True, device="cuda"): # clip-vit-base-patch32 
 super().__init__() 
 
 model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model'] 
 match_params = dict() 
 for key in list(model_state_dict.keys()): 
 if 'caption_encoder' in key: 
 match_params[key.replace('caption_encoder.', '')] = model_state_dict[key] 
 
 config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() 
 args = read_config_as_args(config_as_str, is_config_str=True) 
 
 # To device 
 self.tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model 
 self.caption_encoder = TextEncoder( 
 args.d_proj, args.text_model, args.transformer_embed_dim 
 ).to(device) 
 self.max_objs = 10 
 self.max_length = args.text_len 
 self.device = device 
 self.order_to_label = self.build_order_dict() 
 if freeze: self.freeze() 
 
 print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") 
 
 def freeze(self): 
 self.caption_encoder.base = self.caption_encoder.base.eval() 
 for param in self.caption_encoder.base.parameters(): 
 param.requires_grad = False 
 
 def build_order_dict(self): 
 order2label = {} 
 time_stamps = ['all','start','mid','end'] 
 for i,time_stamp in enumerate(time_stamps): 
 order2label[time_stamp] = i 
 return order2label 
 
 def encode(self, text): 
 obj_list,orders_list = [],[] 
 for raw in text: 
 splits = raw.split('@') # raw example: '<man speaking& order 1 start>@<man speaking& order 2 mid>@<idle engine& all>' 
 objs = [] 
 orders = [] 
 for split in splits:# <obj& order> 
 split = split[1:-1] 
 obj,order = split.split('&') 
 objs.append(obj.strip()) 
 try: 
 orders.append(self.order_to_label[order.strip()]) 
 except: 
 print(order.strip(),raw) 
 assert len(objs) == len(orders) 
 obj_list.append(' | '.join(objs)+' |')# '|' after every word 
 orders_list.append(orders) 
 batch_encoding = self.tokenizer(obj_list, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 tokens = batch_encoding["input_ids"] 
 attn_mask = batch_encoding["attention_mask"] 
 outputs = self.caption_encoder.base(input_ids=tokens.to(self.device)) 
 z = outputs.last_hidden_state 
 return {'token_embedding':z,'token_ids':tokens,'orders':orders_list,'attn_mask':attn_mask} 
 
class FrozenCLAPT5Embedder(AbstractEncoder): 
 """Uses the CLAP transformer encoder for text from microsoft""" 
 def __init__(self, weights_path,t5version="google/flan-t5-large", freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 
 super().__init__() 
 
 model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model'] 
 match_params = dict() 
 for key in list(model_state_dict.keys()): 
 if 'caption_encoder' in key: 
 match_params[key.replace('caption_encoder.', '')] = model_state_dict[key] 
 
 config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yml').read_text() 
 args = read_config_as_args(config_as_str, is_config_str=True) 
 
 self.clap_tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model 
 self.caption_encoder = TextEncoder( 
 args.d_proj, args.text_model, args.transformer_embed_dim 
 ) 
 
 self.t5_tokenizer = T5Tokenizer.from_pretrained(t5version) 
 self.t5_transformer = T5EncoderModel.from_pretrained(t5version) 
 
 self.max_length = max_length 
 self.to(device=device) 
 if freeze: self.freeze() 
 
 print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") 
 
 def freeze(self): 
 self.caption_encoder = self.caption_encoder.eval() 
 for param in self.caption_encoder.parameters(): 
 param.requires_grad = False 
 
 def to(self,device): 
 self.t5_transformer.to(device) 
 self.caption_encoder.to(device) 
 self.device = device 
 
 def encode(self, text): 
 ori_caption = text['ori_caption'] 
 struct_caption = text['struct_caption'] 
 # print(ori_caption,struct_caption) 
 clap_batch_encoding = self.clap_tokenizer(ori_caption, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 ori_tokens = clap_batch_encoding["input_ids"].to(self.device) 
 t5_batch_encoding = self.t5_tokenizer(struct_caption, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 struct_tokens = t5_batch_encoding["input_ids"].to(self.device) 
 outputs = self.caption_encoder.base(input_ids=ori_tokens) 
 z = self.caption_encoder.projection(outputs.last_hidden_state) 
 z2 = self.t5_transformer(input_ids=struct_tokens).last_hidden_state 
 return torch.concat([z,z2],dim=1) 
 
 
class FrozenCLAPFLANEmbedder(AbstractEncoder): 
 """Uses the CLAP transformer encoder for text from microsoft""" 
 def __init__(self, weights_path,t5version="../ldm/modules/encoders/CLAP/t5-v1_1-large", freeze=True, device="cuda", max_length=77): # clip-vit-base-patch32 
 super().__init__() 
 
 model_state_dict = torch.load(weights_path, map_location=torch.device('cpu'))['model'] 
 match_params = dict() 
 for key in list(model_state_dict.keys()): 
 if 'caption_encoder' in key: 
 match_params[key.replace('caption_encoder.', '')] = model_state_dict[key] 
 
 config_as_str = files('ldm').joinpath('modules/encoders/CLAP/config.yaml').read_text() 
 args = read_config_as_args(config_as_str, is_config_str=True) 
 
 self.clap_tokenizer = AutoTokenizer.from_pretrained(args.text_model) # args.text_model 
 self.caption_encoder = TextEncoder( 
 args.d_proj, args.text_model, args.transformer_embed_dim 
 ) 
 
 self.t5_tokenizer = T5Tokenizer.from_pretrained(t5version) 
 self.t5_transformer = T5EncoderModel.from_pretrained(t5version) 
 
 self.max_length = max_length 
 # self.to(device=device) 
 if freeze: self.freeze() 
 
 print(f"{self.caption_encoder.__class__.__name__} comes with {count_params(self.caption_encoder) * 1.e-6:.2f} M params.") 
 
 def freeze(self): 
 self.caption_encoder = self.caption_encoder.eval() 
 for param in self.caption_encoder.parameters(): 
 param.requires_grad = False 
 
 def to(self,device): 
 self.t5_transformer.to(device) 
 self.caption_encoder.to(device) 
 self.device = device 
 
 def encode(self, text): 
 ori_caption = text['ori_caption'] 
 struct_caption = text['struct_caption'] 
 # print(ori_caption,struct_caption) 
 clap_batch_encoding = self.clap_tokenizer(ori_caption, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 ori_tokens = clap_batch_encoding["input_ids"].to(self.device) 
 t5_batch_encoding = self.t5_tokenizer(struct_caption, truncation=True, max_length=self.max_length, return_length=True, 
 return_overflowing_tokens=False, padding="max_length", return_tensors="pt") 
 struct_tokens = t5_batch_encoding["input_ids"].to(self.device) 
 # if self.caption_encoder.device != ori_tokens.device: 
 # self.to(self.device) 
 outputs = self.caption_encoder.base(input_ids=ori_tokens) 
 z = self.caption_encoder.projection(outputs.last_hidden_state) 
 z2 = self.t5_transformer(input_ids=struct_tokens).last_hidden_state 
 return torch.concat([z,z2],dim=1)