class_docstr: COINZSPredictor for COIN zero-shot prediction.
class_docstr_tok: ["COINZSPredictor","for","COIN","zero-shot","prediction","."]
docstr: refactored from line 144:
  https://github.com/DmZhukov/CrossTask/blob/master/train.py
docstr_tok: ["refactored","from","line","144",":","https",":","\/\/github.com\/DmZhukov\/CrossTask\/blob\/master\/train.py"]
code: def predict_loop(
      self, model, eval_dataloader, output_file="result.pkl"
  ):
      """refactored from line 144:
      https://github.com/DmZhukov/CrossTask/blob/master/train.py
      """
      ctx = 0
      model.eval()
      model = model.to(ctx)
  
      with torch.no_grad():
          outputs = (
              eval_dataloader.dataset.meta_processor.meta_text_labels(
                  self.dataset_config
              )
          )
          outputs = self.to_ctx(outputs, ctx)
          label_hidden_states = model.forward_text(**outputs).cpu()
          label_sim = label_hidden_states @ label_hidden_states.t()
          num_labels = label_sim.size(0)
          eye_mask = ~torch.eye(num_labels, dtype=torch.bool)
          label_sim = label_sim.masked_select(eye_mask).view(
              num_labels, num_labels - 1
          )
          lbd = label_sim.max()
  
      # this is not a loss but just compute neg_log_prob.
      Y_pred = []
      Y_true = []
      with torch.no_grad():
          for batch in eval_dataloader:
              self(
                  batch, label_hidden_states, model, lbd, Y_pred, Y_true
              )
      return self.finalize(Y_pred, Y_true, output_file)
  
imports: import os
  import random
  import json
  import numpy
  import torch
  import pickle
  import math
  from tqdm import tqdm
label_desc: Use image node_id 2 for calling the COINZSPredictor obj's underlying member method code with example usage: obj.predict_loop(model, eval_dataloader, output_file) and returns: self