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from typing import List, Dict, Optional
import torch
from PIL.Image import Image
from torch.utils.data import DataLoader
from datasets import Dataset
from transformers import AutoModel, AutoProcessor
MODEL_NAME = "Marqo/marqo-fashionCLIP"
class FashionCLIPEncoder:
def __init__(self):
self.processor = AutoProcessor.from_pretrained(
MODEL_NAME, trust_remote_code=True
)
self.model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True)
self.model.eval()
self.device = self.model.device
def encode_images(
self, images: List[Image], batch_size: Optional[int] = None
) -> List[List[float]]:
if batch_size is None:
batch_size = len(images)
def transform_fn(el: Dict):
return self.processor(
images=[content for content in el["image"]], return_tensors="pt"
)
dataset = Dataset.from_dict({"image": images})
dataset.set_format("torch")
dataset.set_transform(transform_fn)
dataloader = DataLoader(dataset, batch_size=batch_size)
image_embeddings = []
with torch.no_grad():
for batch in dataloader:
batch = {k: v.to(self.device) for k, v in batch.items()}
embeddings = self._encode_images(batch)
image_embeddings.extend(embeddings)
return image_embeddings
def encode_text(
self, text: List[str], batch_size: Optional[int] = None
) -> List[List[float]]:
if batch_size is None:
batch_size = len(text)
def transform_fn(el: Dict):
kwargs = {
"padding": "max_length",
"return_tensors": "pt",
"truncation": True,
}
return self.processor(text=el["text"], **kwargs)
dataset = Dataset.from_dict({"text": text})
dataset = dataset.map(
function=transform_fn, batched=True, remove_columns=["text"]
)
dataset.set_format("torch")
dataloader = DataLoader(dataset, batch_size=batch_size)
text_embeddings = []
with torch.no_grad():
for batch in dataloader:
batch = {k: v.to(self.device) for k, v in batch.items()}
embeddings = self._encode_text(batch)
text_embeddings.extend(embeddings)
return text_embeddings
def _encode_images(self, batch: Dict) -> List:
return self.model.get_image_features(**batch).detach().cpu().numpy().tolist()
def _encode_text(self, batch: Dict) -> List:
return self.model.get_text_features(**batch).detach().cpu().numpy().tolist() |