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Prevent unwanted log messages from modules

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oobabooga 2023-05-21 22:42:34 -03:00
parent fb91406e93
commit e116d31180
20 changed files with 120 additions and 111 deletions
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15
extensions/multimodal/pipelines/llava/llava.py
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@ -1,16 +1,17 @@
import logging
import time
from abc import abstractmethod
from typing import List, Tuple
import torch
from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline
from huggingface_hub import hf_hub_download
from modules import shared
from modules.text_generation import encode
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionModel
from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline
from modules import shared
from modules.logging_colors import logger
from modules.text_generation import encode
class LLaVA_v0_Pipeline(AbstractMultimodalPipeline):
CLIP_REPO = "openai/clip-vit-large-patch14"
@ -26,11 +27,11 @@ class LLaVA_v0_Pipeline(AbstractMultimodalPipeline):
def _load_models(self):
start_ts = time.time()
logging.info(f"LLaVA - Loading CLIP from {LLaVA_v0_Pipeline.CLIP_REPO} as {self.clip_dtype} on {self.clip_device}...")
logger.info(f"LLaVA - Loading CLIP from {LLaVA_v0_Pipeline.CLIP_REPO} as {self.clip_dtype} on {self.clip_device}...")
image_processor = CLIPImageProcessor.from_pretrained(LLaVA_v0_Pipeline.CLIP_REPO, torch_dtype=self.clip_dtype)
vision_tower = CLIPVisionModel.from_pretrained(LLaVA_v0_Pipeline.CLIP_REPO, torch_dtype=self.clip_dtype).to(self.clip_device)
logging.info(f"LLaVA - Loading projector from {self.llava_projector_repo()} as {self.projector_dtype} on {self.projector_device}...")
logger.info(f"LLaVA - Loading projector from {self.llava_projector_repo()} as {self.projector_dtype} on {self.projector_device}...")
projector_path = hf_hub_download(self.llava_projector_repo(), self.llava_projector_filename())
mm_projector = torch.nn.Linear(*self.llava_projector_shape())
projector_data = torch.load(projector_path)
@ -38,7 +39,7 @@ class LLaVA_v0_Pipeline(AbstractMultimodalPipeline):
mm_projector.bias = torch.nn.Parameter(projector_data['model.mm_projector.bias'].to(dtype=self.projector_dtype), False)
mm_projector = mm_projector.to(self.projector_device)
logging.info(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds")
logger.info(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds")
return image_processor, vision_tower, mm_projector
@staticmethod