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Running
on
Zero
File size: 3,354 Bytes
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from typing import Dict, List, Optional
import torch
import torch.nn as nn
from transformers import AutoProcessor, AutoModel
from multi_token.data_tools import load_video
from multi_token.modalities.base_modality import Modality
from multi_token.modalities.projectors import (
build_mlp_vector_projector,
)
OUTPUT_EMB_SIZE = 512
class XCLIPVideoModule(nn.Module):
def __init__(self, model_name_or_path: str):
super().__init__()
self.model_name_or_path = model_name_or_path
self.model = None
self.processor = None
self.load_model()
def load_model(self):
self.model = AutoModel.from_pretrained(self.model_name_or_path)
self.processor = AutoProcessor.from_pretrained(self.model_name_or_path)
self.model.requires_grad_(False)
@torch.no_grad()
def forward(self, video_inputs) -> torch.Tensor:
with torch.no_grad():
outputs = self.model(**(video_inputs.to(device=self.device)))
emb = outputs.video_embeds.to(device=self.device, dtype=self.dtype).view(
-1, 1, OUTPUT_EMB_SIZE
)
return emb
@property
def dtype(self):
return self.model.dtype
@property
def device(self):
return self.model.device
class XCLIPVideoModality(Modality):
def __init__(
self,
model_name_or_path: str = "microsoft/xclip-base-patch32",
num_projector_layers: int = 2,
num_tokens_output: int = 10,
):
self.model_name_or_path = model_name_or_path
self.module = XCLIPVideoModule(model_name_or_path=self.model_name_or_path)
self.num_projector_layers = num_projector_layers
self.num_tokens_output = num_tokens_output
def build_projector(self, lm_hidden_size: int) -> nn.Module:
return build_mlp_vector_projector(
input_hidden_size=OUTPUT_EMB_SIZE,
lm_hidden_size=lm_hidden_size,
num_layers=self.num_projector_layers,
num_tokens=self.num_tokens_output,
)
@property
def name(self) -> str:
return "video_xclip"
@property
def token(self) -> str:
return "<video>"
@property
def data_key(self) -> str:
return "videos"
@property
def token_width(self) -> int:
return self.num_tokens_output
def to(self, dtype: torch.dtype, device: torch.device) -> "XCLIPVideoModality":
self.module.to(dtype=dtype, device=device)
return self
def preprocess_rows(self, rows: List[Dict]) -> List[Optional[Dict]]:
row_values = []
for row in rows:
video_arrays = [
load_video(
video_info,
)
for video_info in row[self.data_key]
]
videos_enc = self.module.processor(
videos=[list(video) for video in video_arrays],
text=["IGNORE"],
return_tensors="pt",
padding=True,
)
row_values.append(videos_enc)
return row_values
@torch.no_grad()
def forward(self, encoded_values: List[torch.Tensor]) -> List[torch.Tensor]:
video_features = []
for video_batch in encoded_values:
video_features.append(self.module.forward(video_batch))
return video_features
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