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import os.path
import torch
import torch.nn as nn
from functools import partial
from transformers import T5Tokenizer, T5EncoderModel, 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
from ldm.util import count_params
import numpy as np
class Video_Feat_Encoder_NoPosembed(nn.Module):
""" Transform the video feat encoder"""
def __init__(self, origin_dim, embed_dim, seq_len=40):
super().__init__()
self.embedder = nn.Sequential(nn.Linear(origin_dim, embed_dim))
def forward(self, x):
# Revise the shape here:
x = self.embedder(x) # B x 117 x C
return x
class Video_Feat_Encoder_NoPosembed_inpaint(Video_Feat_Encoder_NoPosembed):
""" Transform the video feat encoder"""
def forward(self, x):
# Revise the shape here:
video, spec = x['mix_video_feat'], x['mix_spec']
video = self.embedder(video) # B x 117 x C
return (video, spec)
class AbstractEncoder(nn.Module):
def __init__(self):
super().__init__()
def encode(self, *args, **kwargs):
raise NotImplementedError
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 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)
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 FrozenCLAPFLANEmbedder(AbstractEncoder):
"""Uses the CLAP transformer encoder for text from microsoft"""
def __init__(self, weights_path, t5version="google/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.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)
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