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# MIT License
# Copyright (c) Microsoft
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Copyright (c) [2025] [Microsoft]
# SPDX-License-Identifier: MIT
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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