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| """ | |
| This script provides an example to wrap TencentPretrain for classification inference. | |
| """ | |
| import sys | |
| import os | |
| import torch | |
| import argparse | |
| import collections | |
| import torch.nn as nn | |
| tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) | |
| sys.path.append(tencentpretrain_dir) | |
| from tencentpretrain.utils.constants import * | |
| from tencentpretrain.utils import * | |
| from tencentpretrain.utils.config import load_hyperparam | |
| from tencentpretrain.utils.seed import set_seed | |
| from tencentpretrain.model_loader import load_model | |
| from tencentpretrain.opts import infer_opts, tokenizer_opts | |
| from finetune.run_classifier import Classifier | |
| def batch_loader(batch_size, src, seg): | |
| instances_num = src.size()[0] | |
| for i in range(instances_num // batch_size): | |
| src_batch = src[i * batch_size : (i + 1) * batch_size, :] | |
| seg_batch = seg[i * batch_size : (i + 1) * batch_size, :] | |
| yield src_batch, seg_batch | |
| if instances_num > instances_num // batch_size * batch_size: | |
| src_batch = src[instances_num // batch_size * batch_size :, :] | |
| seg_batch = seg[instances_num // batch_size * batch_size :, :] | |
| yield src_batch, seg_batch | |
| def read_dataset(args, path): | |
| dataset, columns = [], {} | |
| with open(path, mode="r", encoding="utf-8") as f: | |
| for line_id, line in enumerate(f): | |
| if line_id == 0: | |
| line = line.rstrip("\r\n").split("\t") | |
| for i, column_name in enumerate(line): | |
| columns[column_name] = i | |
| continue | |
| line = line.rstrip("\r\n").split("\t") | |
| if "text_b" not in columns: # Sentence classification. | |
| text_a = line[columns["text_a"]] | |
| src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN]) | |
| seg = [1] * len(src) | |
| else: # Sentence pair classification. | |
| text_a, text_b = line[columns["text_a"]], line[columns["text_b"]] | |
| src_a = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN]) | |
| src_b = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_b) + [SEP_TOKEN]) | |
| src = src_a + src_b | |
| seg = [1] * len(src_a) + [2] * len(src_b) | |
| if len(src) > args.seq_length: | |
| src = src[: args.seq_length] | |
| seg = seg[: args.seq_length] | |
| PAD_ID = args.tokenizer.convert_tokens_to_ids([PAD_TOKEN])[0] | |
| while len(src) < args.seq_length: | |
| src.append(PAD_ID) | |
| seg.append(0) | |
| dataset.append((src, seg)) | |
| return dataset | |
| def main(): | |
| parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) | |
| infer_opts(parser) | |
| parser.add_argument("--labels_num", type=int, required=True, | |
| help="Number of prediction labels.") | |
| tokenizer_opts(parser) | |
| parser.add_argument("--output_logits", action="store_true", help="Write logits to output file.") | |
| parser.add_argument("--output_prob", action="store_true", help="Write probabilities to output file.") | |
| args = parser.parse_args() | |
| # Load the hyperparameters from the config file. | |
| args = load_hyperparam(args) | |
| # Build tokenizer. | |
| args.tokenizer = str2tokenizer[args.tokenizer](args) | |
| # Build classification model and load parameters. | |
| args.soft_targets, args.soft_alpha = False, False | |
| model = Classifier(args) | |
| model = load_model(model, args.load_model_path) | |
| # For simplicity, we use DataParallel wrapper to use multiple GPUs. | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = model.to(device) | |
| if torch.cuda.device_count() > 1: | |
| print("{} GPUs are available. Let's use them.".format(torch.cuda.device_count())) | |
| model = torch.nn.DataParallel(model) | |
| dataset = read_dataset(args, args.test_path) | |
| src = torch.LongTensor([sample[0] for sample in dataset]) | |
| seg = torch.LongTensor([sample[1] for sample in dataset]) | |
| batch_size = args.batch_size | |
| instances_num = src.size()[0] | |
| print("The number of prediction instances: ", instances_num) | |
| model.eval() | |
| with open(args.prediction_path, mode="w", encoding="utf-8") as f: | |
| f.write("label") | |
| if args.output_logits: | |
| f.write("\t" + "logits") | |
| if args.output_prob: | |
| f.write("\t" + "prob") | |
| f.write("\n") | |
| for i, (src_batch, seg_batch) in enumerate(batch_loader(batch_size, src, seg)): | |
| src_batch = src_batch.to(device) | |
| seg_batch = seg_batch.to(device) | |
| with torch.no_grad(): | |
| _, logits = model(src_batch, None, seg_batch) | |
| pred = torch.argmax(logits, dim=1) | |
| pred = pred.cpu().numpy().tolist() | |
| prob = nn.Softmax(dim=1)(logits) | |
| logits = logits.cpu().numpy().tolist() | |
| prob = prob.cpu().numpy().tolist() | |
| for j in range(len(pred)): | |
| f.write(str(pred[j])) | |
| if args.output_logits: | |
| f.write("\t" + " ".join([str(v) for v in logits[j]])) | |
| if args.output_prob: | |
| f.write("\t" + " ".join([str(v) for v in prob[j]])) | |
| f.write("\n") | |
| if __name__ == "__main__": | |
| main() | |