Delete pipeline.py

pipeline.py

@@ -1,73 +0,0 @@
-#! /usr/bin/env python
-# -*- coding: utf-8 -*-
-
-# Copyright 2023 Imperial College London (Pingchuan Ma)
-# Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-import os
-import torch
-import pickle
-from configparser import ConfigParser
-
-from pipelines.model import AVSR
-from pipelines.data.data_module import AVSRDataLoader
-
-
-class InferencePipeline(torch.nn.Module):
-    def __init__(self, config_filename, detector="retinaface", face_track=False, device="cuda:0"):
-        super(InferencePipeline, self).__init__()
-        assert os.path.isfile(config_filename), f"config_filename: {config_filename} does not exist."
-
-        config = ConfigParser()
-        config.read(config_filename)
-
-        # modality configuration
-        modality = config.get("input", "modality")
-
-        self.modality = modality
-        # data configuration
-        input_v_fps = config.getfloat("input", "v_fps")
-        model_v_fps = config.getfloat("model", "v_fps")
-
-        # model configuration
-        model_path = config.get("model","model_path")
-        model_conf = config.get("model","model_conf")
-
-        # language model configuration
-        rnnlm = config.get("model", "rnnlm")
-        rnnlm_conf = config.get("model", "rnnlm_conf")
-        penalty = config.getfloat("decode", "penalty")
-        ctc_weight = config.getfloat("decode", "ctc_weight")
-        lm_weight = config.getfloat("decode", "lm_weight")
-        beam_size = config.getint("decode", "beam_size")
-
-        self.dataloader = AVSRDataLoader(modality, speed_rate=input_v_fps/model_v_fps, detector=detector)
-        self.model = AVSR(modality, model_path, model_conf, rnnlm, rnnlm_conf, penalty, ctc_weight, lm_weight, beam_size, device)
-        if face_track and self.modality in ["video", "audiovisual"]:
-            if detector == "mediapipe":
-                from pipelines.detectors.mediapipe.detector import LandmarksDetector
-                self.landmarks_detector = LandmarksDetector()
-            if detector == "retinaface":
-                from pipelines.detectors.retinaface.detector import LandmarksDetector
-                self.landmarks_detector = LandmarksDetector(device="cuda:0")
-        else:
-            self.landmarks_detector = None
-
-
-    def process_landmarks(self, data_filename, landmarks_filename):
-        if self.modality == "audio":
-            return None
-        if self.modality in ["video", "audiovisual"]:
-            if isinstance(landmarks_filename, str):
-                landmarks = pickle.load(open(landmarks_filename, "rb"))
-            else:
-                landmarks = self.landmarks_detector(data_filename)
-            return landmarks
-
-
-    def forward(self, data_filename, landmarks_filename=None):
-        assert os.path.isfile(data_filename), f"data_filename: {data_filename} does not exist."
-        landmarks = self.process_landmarks(data_filename, landmarks_filename)
-        data = self.dataloader.load_data(data_filename, landmarks)
-        transcript = self.model.infer(data)
-        return transcript