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# -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
# FileName [ src/runner.py ]
# Synopsis [ scripts for running pre-training and downstream evaluation of transformer models ]
# Author [ Andy T. Liu (Andi611) ]
# Copyright [ Copyleft(c), Speech Lab, NTU, Taiwan ]
"""*********************************************************************************************"""
"""
WARNING:
This script is deprecated,
we suggest you use the new scripts of: `run_upstream.py` and `run_downstream.py`
"""
###############
# IMPORTATION #
###############
import yaml
import torch
import random
import argparse
import numpy as np
from utility.helper import parse_prune_heads
#########################
# RUNNER CONFIGURATIONS #
#########################
def get_runner_args():
parser = argparse.ArgumentParser(description='Argument Parser for the S3PLR project.')
# setting
parser.add_argument('--config', default='../config/deprecated_runner/tera_libri_fmllrBase_pretrain,yaml', type=str, help='Path to experiment config.', required=False)
parser.add_argument('--seed', default=1337, type=int, help='Random seed for reproducable results.', required=False)
# Logging
parser.add_argument('--logdir', default='../log/log_transformer/', type=str, help='Logging path.', required=False)
parser.add_argument('--name', default=None, type=str, help='Name for logging.', required=False)
# model ckpt
parser.add_argument('--load', action='store_true', help='Load pre-trained model to restore training, no need to specify this during testing.')
parser.add_argument('--ckpdir', default='../result/result_transformer/', type=str, help='path to store experiment result.', required=False)
parser.add_argument('--ckpt', default='fmllrBase960-F-N-K-libri/states-1000000.ckpt', type=str, help='path to transformer model checkpoint.', required=False)
parser.add_argument('--dckpt', default='baseline_sentiment_libri_sd1337/baseline_sentiment-500000.ckpt', type=str, help='path to downstream checkpoint.', required=False)
parser.add_argument('--apc_path', default='../result/result_apc/apc_libri_sd1337_standard/apc-500000.ckpt', type=str, help='path to the apc model checkpoint.', required=False)
# mockingjay
parser.add_argument('--train', action='store_true', help='Train the model.')
parser.add_argument('--run_transformer', action='store_true', help='train and test the downstream tasks using speech representations.')
parser.add_argument('--run_apc', action='store_true', help='train and test the downstream tasks using apc representations.')
parser.add_argument('--fine_tune', action='store_true', help='fine tune the transformer model with downstream task.')
parser.add_argument('--plot', action='store_true', help='Plot model generated results during testing.')
# phone task
parser.add_argument('--train_phone', action='store_true', help='Train the phone classifier on mel or speech representations.')
parser.add_argument('--test_phone', action='store_true', help='Test mel or speech representations using the trained phone classifier.')
# cpc phone task
parser.add_argument('--train_cpc_phone', action='store_true', help='Train the phone classifier on mel or speech representations with the alignments in CPC paper.')
parser.add_argument('--test_cpc_phone', action='store_true', help='Test mel or speech representations using the trained phone classifier with the alignments in CPC paper.')
# sentiment task
parser.add_argument('--train_sentiment', action='store_true', help='Train the sentiment classifier on mel or speech representations.')
parser.add_argument('--test_sentiment', action='store_true', help='Test mel or speech representations using the trained sentiment classifier.')
# speaker verification task
parser.add_argument('--train_speaker', action='store_true', help='Train the speaker classifier on mel or speech representations.')
parser.add_argument('--test_speaker', action='store_true', help='Test mel or speech representations using the trained speaker classifier.')
# Options
parser.add_argument('--with_head', action='store_true', help='inference with the spectrogram head, the model outputs spectrogram.')
parser.add_argument('--plot_attention', action='store_true', help='plot attention')
parser.add_argument('--load_ws', default='result/result_transformer_sentiment/10111754-10170300-weight_sum/best_val.ckpt', help='load weighted-sum weights from trained downstream model')
parser.add_argument('--cpu', action='store_true', help='Disable GPU training.')
parser.add_argument('--multi_gpu', action='store_true', help='Enable Multi-GPU training.')
parser.add_argument('--no_msg', action='store_true', help='Hide all messages.')
parser.add_argument('--test_reconstruct', action='store_true', help='Test reconstruction capability')
# parse
args = parser.parse_args()
setattr(args,'gpu', not args.cpu)
setattr(args,'verbose', not args.no_msg)
config = yaml.load(open(args.config,'r'), Loader=yaml.FullLoader)
parse_prune_heads(config)
return config, args
########
# MAIN #
########
def main():
# get arguments
config, args = get_runner_args()
# Fix seed and make backends deterministic
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available(): torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Train Transformer
if args.train:
from transformer.solver import Trainer
trainer = Trainer(config, args)
trainer.load_data(split='train')
trainer.set_model(inference=False)
trainer.exec()
# Test Transformer
if args.test_reconstruct:
from transformer.solver import Trainer
trainer = Trainer(config, args)
trainer.load_data(split='test')
trainer.set_model(inference=True, with_head=True)
trainer.test_reconstruct()
##################################################################################
# Train Phone Task
elif args.train_phone:
from downstream.solver import Downstream_Trainer
task = 'transformer_phone' if args.run_transformer \
else 'apc_phone' if args.run_apc else 'baseline_phone'
trainer = Downstream_Trainer(config, args, task=task)
trainer.load_data(split='train', load='montreal_phone')
trainer.set_model(inference=False)
trainer.exec()
# Test Phone Task
elif args.test_phone:
from downstream.solver import Downstream_Tester
task = 'transformer_phone' if args.run_transformer \
else 'apc_phone' if args.run_apc else 'baseline_phone'
tester = Downstream_Tester(config, args, task=task)
tester.load_data(split='test', load='montreal_phone')
tester.set_model(inference=True)
tester.exec()
##################################################################################
# Train the CPC Phone Task
elif args.train_cpc_phone:
from downstream.solver import Downstream_Trainer
task = 'transformer_cpc_phone' if args.run_transformer \
else 'apc_cpc_phone' if args.run_apc else 'baseline_cpc_phone'
trainer = Downstream_Trainer(config, args, task=task)
trainer.load_data(split='train', load='cpc_phone')
trainer.set_model(inference=False)
trainer.exec()
# Test Phone Task
elif args.test_cpc_phone:
from downstream.solver import Downstream_Tester
task = 'transformer_cpc_phone' if args.run_transformer \
else 'apc_cpc_phone' if args.run_apc else 'baseline_cpc_phone'
tester = Downstream_Tester(config, args, task=task)
tester.load_data(split='test', load='cpc_phone')
tester.set_model(inference=True)
tester.exec()
##################################################################################
# Train Sentiment Task
elif args.train_sentiment:
from downstream.solver import Downstream_Trainer
task = 'transformer_sentiment' if args.run_transformer \
else 'apc_sentiment' if args.run_apc else 'baseline_sentiment'
trainer = Downstream_Trainer(config, args, task=task)
trainer.load_data(split='train', load='sentiment')
trainer.set_model(inference=False)
trainer.exec()
# Test Sentiment Task
elif args.test_sentiment:
from downstream.solver import Downstream_Tester
task = 'transformer_sentiment' if args.run_transformer \
else 'apc_sentiment' if args.run_apc else 'baseline_sentiment'
tester = Downstream_Tester(config, args, task=task)
tester.load_data(split='test', load='sentiment')
tester.set_model(inference=True)
tester.exec()
##################################################################################
# Train Speaker Task
elif args.train_speaker:
from downstream.solver import Downstream_Trainer
task = 'transformer_speaker' if args.run_transformer \
else 'apc_speaker' if args.run_apc else 'baseline_speaker'
trainer = Downstream_Trainer(config, args, task=task)
trainer.load_data(split='train', load='speaker')
# trainer.load_data(split='train', load='speaker_large') # Deprecated
trainer.set_model(inference=False)
trainer.exec()
# Test Speaker Task
elif args.test_speaker:
from downstream.solver import Downstream_Tester
task = 'transformer_speaker' if args.run_transformer \
else 'apc_speaker' if args.run_apc else 'baseline_speaker'
tester = Downstream_Tester(config, args, task=task)
tester.load_data(split='test', load='speaker')
# tester.load_data(split='test', load='speaker_large') # Deprecated
tester.set_model(inference=True)
tester.exec()
##################################################################################
# Visualize Transformer
elif args.plot:
from transformer.solver import Tester
tester = Tester(config, args)
tester.load_data(split='test', load_mel_only=True)
tester.set_model(inference=True, with_head=args.with_head)
tester.plot(with_head=args.with_head)
elif args.plot_attention:
from transformer.solver import Tester
tester = Tester(config, args)
tester.load_data(split='test', load_mel_only=True)
tester.set_model(inference=True, output_attention=True)
tester.plot_attention()
if __name__ == '__main__':
main() |