DeepLearningExamples/PyTorch/SpeechRecognition/Jasper/inference.py

# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#           http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import itertools
from typing import List
from tqdm import tqdm
import math
import toml
from dataset import AudioToTextDataLayer
from helpers import process_evaluation_batch, process_evaluation_epoch, Optimization, add_ctc_labels, AmpOptimizations, print_dict, model_multi_gpu
from model import AudioPreprocessing, GreedyCTCDecoder, JasperEncoderDecoder
import torch
import apex
from apex import amp
import random
import numpy as np
import pickle

def parse_args():
    parser = argparse.ArgumentParser(description='Jasper')
    parser.add_argument("--local_rank", default=None, type=int)
    parser.add_argument("--batch_size", default=16, type=int, help='data batch size')
    parser.add_argument("--steps", default=None, help='if not specified do evaluation on full dataset. otherwise only evaluates the specified number of iterations for each worker', type=int)
    parser.add_argument("--model_toml", type=str, help='relative model configuration path given dataset folder')
    parser.add_argument("--dataset_dir", type=str, help='absolute path to dataset folder')
    parser.add_argument("--val_manifest", type=str, help='relative path to evaluation dataset manifest file')
    parser.add_argument("--ckpt", default=None, type=str, required=True, help='path to model checkpoint')
    parser.add_argument("--max_duration", default=None, type=float, help='maximum duration of sequences. if None uses attribute from model configuration file')
    parser.add_argument("--pad_to", default=None, type=int, help="default is pad to value as specified in model configurations. if -1 pad to maximum duration. If > 0 pad batch to next multiple of value")
    parser.add_argument("--fp16", action='store_true', help='use half precision')
    parser.add_argument("--cudnn_benchmark", action='store_true', help="enable cudnn benchmark")
    parser.add_argument("--save_prediction", type=str, default=None, help="if specified saves predictions in text form at this location")
    parser.add_argument("--logits_save_to", default=None, type=str, help="if specified will save logits to path")
    parser.add_argument("--seed", default=42, type=int, help='seed')
    return parser.parse_args()

def eval(
        data_layer,
        audio_processor, 
        encoderdecoder, 
        greedy_decoder, 
        labels, 
        multi_gpu,
        args):
    """performs inference / evaluation
    Args:
        data_layer: data layer object that holds data loader
        audio_processor: data processing module
        encoderdecoder: acoustic model
        greedy_decoder: greedy decoder
        labels: list of labels as output vocabulary
        multi_gpu: true if using multiple gpus
        args: script input arguments
    """
    logits_save_to=args.logits_save_to
    audio_processor.eval()
    encoderdecoder.eval()
    with torch.no_grad():
        _global_var_dict = {
            'predictions': [],
            'transcripts': [],
            'logits' : [],
        }

        for it, data in enumerate(tqdm(data_layer.data_iterator)):
            tensors = []
            for d in data:
                tensors.append(d.cuda())

            t_audio_signal_e, t_a_sig_length_e, t_transcript_e, t_transcript_len_e = tensors

            inp = (t_audio_signal_e, t_a_sig_length_e)

            t_processed_signal, p_length_e = audio_processor(x=inp) 
            t_log_probs_e, _ = encoderdecoder((t_processed_signal, p_length_e))
            t_predictions_e = greedy_decoder(log_probs=t_log_probs_e)

            values_dict = dict(
                predictions=[t_predictions_e],
                transcript=[t_transcript_e],
                transcript_length=[t_transcript_len_e],
                output=[t_log_probs_e]
            )
            process_evaluation_batch(values_dict, _global_var_dict, labels=labels)

            if args.steps is not None and it + 1 >= args.steps:
                break
        wer, _ = process_evaluation_epoch(_global_var_dict)
        if (not multi_gpu or (multi_gpu and torch.distributed.get_rank() == 0)):    
            print("==========>>>>>>Evaluation WER: {0}\n".format(wer))
            if args.save_prediction is not None:
                with open(args.save_prediction, 'w') as fp:
                    fp.write('\n'.join(_global_var_dict['predictions']))
            if logits_save_to is not None:
                logits = []
                for batch in _global_var_dict["logits"]:
                    for i in range(batch.shape[0]):
                        logits.append(batch[i].cpu().numpy())
                with open(logits_save_to, 'wb') as f:
                    pickle.dump(logits, f, protocol=pickle.HIGHEST_PROTOCOL)

def main(args):
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.backends.cudnn.benchmark = args.cudnn_benchmark
    print("CUDNN BENCHMARK ", args.cudnn_benchmark)
    assert(torch.cuda.is_available())

    if args.local_rank is not None:
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(backend='nccl', init_method='env://')
    multi_gpu = args.local_rank is not None 
    if multi_gpu:
        print("DISTRIBUTED with ", torch.distributed.get_world_size())

    if args.fp16:
        optim_level = Optimization.mxprO3
    else:
        optim_level = Optimization.mxprO0

    jasper_model_definition = toml.load(args.model_toml)
    dataset_vocab = jasper_model_definition['labels']['labels']
    ctc_vocab = add_ctc_labels(dataset_vocab)

    val_manifest = args.val_manifest 
    featurizer_config = jasper_model_definition['input_eval']
    featurizer_config["optimization_level"] = optim_level

    if args.max_duration is not None:
        featurizer_config['max_duration'] = args.max_duration
    if args.pad_to is not None:
        featurizer_config['pad_to'] = args.pad_to if args.pad_to >= 0 else "max"

    print('model_config')
    print_dict(jasper_model_definition)
    print('feature_config')
    print_dict(featurizer_config)

    data_layer = AudioToTextDataLayer(
                                    dataset_dir=args.dataset_dir, 
                                    featurizer_config=featurizer_config,
                                    manifest_filepath=val_manifest,
                                    labels=dataset_vocab,
                                    batch_size=args.batch_size,
                                    pad_to_max=featurizer_config['pad_to'] == "max",
                                    shuffle=False,
                                    multi_gpu=multi_gpu)
    audio_preprocessor = AudioPreprocessing(**featurizer_config)

    encoderdecoder = JasperEncoderDecoder(jasper_model_definition=jasper_model_definition, feat_in=1024, num_classes=len(ctc_vocab))
 
    if args.ckpt is not None:
        print("loading model from ", args.ckpt)
        checkpoint = torch.load(args.ckpt, map_location="cpu")
        for k in audio_preprocessor.state_dict().keys():
            checkpoint['state_dict'][k] = checkpoint['state_dict'].pop("audio_preprocessor." + k)
        audio_preprocessor.load_state_dict(checkpoint['state_dict'], strict=False)
        encoderdecoder.load_state_dict(checkpoint['state_dict'], strict=False)
    
    greedy_decoder = GreedyCTCDecoder()

    # print("Number of parameters in encoder: {0}".format(model.jasper_encoder.num_weights()))

    N = len(data_layer)
    step_per_epoch = math.ceil(N / (args.batch_size * (1 if not torch.distributed.is_initialized() else torch.distributed.get_world_size())))

    if args.steps is not None:
        print('-----------------')
        print('Have {0} examples to eval on.'.format(args.steps * args.batch_size * (1 if not torch.distributed.is_initialized() else torch.distributed.get_world_size())))
        print('Have {0} steps / (gpu * epoch).'.format(args.steps))
        print('-----------------')
    else:
        print('-----------------')
        print('Have {0} examples to eval on.'.format(N))
        print('Have {0} steps / (gpu * epoch).'.format(step_per_epoch))
        print('-----------------')

    audio_preprocessor.cuda()
    encoderdecoder.cuda()
    if args.fp16:
        encoderdecoder = amp.initialize(
            models=encoderdecoder,
            opt_level=AmpOptimizations[optim_level])

    encoderdecoder = model_multi_gpu(encoderdecoder, multi_gpu)

    eval(
        data_layer=data_layer, 
        audio_processor=audio_preprocessor,
        encoderdecoder=encoderdecoder,
        greedy_decoder=greedy_decoder,
        labels=ctc_vocab,
        args=args,
        multi_gpu=multi_gpu)

if __name__=="__main__":
    args = parse_args()
    
    print_dict(vars(args))

    main(args)