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from typing import *
import torch
import torch.nn as nn
from .. import models
class Pipeline:
"""
A base class for pipelines.
"""
def __init__(
self,
models: dict[str, nn.Module] = None,
):
if models is None:
return
self.models = models
for model in self.models.values():
model.eval()
@staticmethod
def from_pretrained(path: str) -> "Pipeline":
"""
Load a pretrained model.
"""
import os
import json
is_local = os.path.exists(f"{path}/pipeline.json")
if is_local:
config_file = f"{path}/pipeline.json"
else:
from huggingface_hub import hf_hub_download
config_file = hf_hub_download(path, "pipeline.json")
with open(config_file, 'r') as f:
args = json.load(f)['args']
_models = {
k: models.from_pretrained(f"{path}/{v}")
for k, v in args['models'].items()
}
new_pipeline = Pipeline(_models)
new_pipeline._pretrained_args = args
return new_pipeline
@property
def device(self) -> torch.device:
for model in self.models.values():
if hasattr(model, 'device'):
return model.device
for model in self.models.values():
if hasattr(model, 'parameters'):
return next(model.parameters()).device
raise RuntimeError("No device found.")
def to(self, device: torch.device) -> None:
for model in self.models.values():
model.to(device)
def cuda(self) -> None:
self.to(torch.device("cuda"))
def cpu(self) -> None:
self.to(torch.device("cpu"))
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