183 lines
6.9 KiB
Python
183 lines
6.9 KiB
Python
# Copyright (c) 2020, 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 json
|
|
import math
|
|
import os
|
|
|
|
import torch
|
|
import torch.distributed as dist
|
|
|
|
from .iterator import DaliIterator, SyntheticDataIterator
|
|
from .pipeline import make_dali_asr_pipeline
|
|
from common.helpers import print_once
|
|
|
|
|
|
def _parse_json(json_path: str, start_label=0, predicate=lambda json: True):
|
|
"""
|
|
Parses json file to the format required by DALI.
|
|
|
|
Args:
|
|
json_path: path to json file
|
|
start_label: the label, starting from which DALI will assign
|
|
consecutive int numbers to every transcript
|
|
predicate: function, that accepts a sample descriptor
|
|
(i.e. json dictionary) as an argument. If the predicate for a given
|
|
sample returns True, it will be included in the dataset.
|
|
|
|
Returns:
|
|
output_files: dict that maps file name to label assigned by DALI
|
|
transcripts: dict that maps label assigned by DALI to the transcript
|
|
"""
|
|
global cnt
|
|
with open(json_path) as f:
|
|
librispeech_json = json.load(f)
|
|
output_files = {}
|
|
transcripts = {}
|
|
curr_label = start_label
|
|
for original_sample in librispeech_json:
|
|
if not predicate(original_sample):
|
|
continue
|
|
transcripts[curr_label] = original_sample['transcript']
|
|
output_files[original_sample['files'][-1]['fname']] = curr_label
|
|
curr_label += 1
|
|
return output_files, transcripts
|
|
|
|
|
|
def _dict_to_file(dict: dict, filename: str):
|
|
with open(filename, "w") as f:
|
|
for key, value in dict.items():
|
|
f.write("{} {}\n".format(key, value))
|
|
|
|
|
|
class DaliDataLoader:
|
|
"""
|
|
DataLoader is the main entry point to the data preprocessing pipeline.
|
|
To use, create an object and then just iterate over `data_iterator`.
|
|
DataLoader will do the rest for you.
|
|
Example:
|
|
data_layer = DataLoader(DaliTrainPipeline, path, json, bs, ngpu)
|
|
data_it = data_layer.data_iterator
|
|
for data in data_it:
|
|
print(data) # Here's your preprocessed data
|
|
|
|
Args:
|
|
device_type: Which device to use for preprocessing. Choose: "cpu", "gpu"
|
|
pipeline_type: Choose: "train", "val", "synth"
|
|
"""
|
|
def __init__(self, gpu_id, dataset_path: str, config_data: dict,
|
|
config_features: dict, json_names: list, symbols: list,
|
|
batch_size: int, pipeline_type: str,
|
|
grad_accumulation_steps: int = 1,
|
|
synth_iters_per_epoch: int = 544, device_type: str = "gpu"):
|
|
|
|
self.batch_size = batch_size
|
|
self.grad_accumulation_steps = grad_accumulation_steps
|
|
self.drop_last = (pipeline_type == 'train')
|
|
self.device_type = device_type
|
|
pipeline_type = self._parse_pipeline_type(pipeline_type)
|
|
if pipeline_type == "synth":
|
|
self._dali_data_iterator = self._init_synth_iterator(
|
|
self.batch_size,
|
|
config_features['nfilt'],
|
|
iters_per_epoch=synth_iters_per_epoch,
|
|
ngpus=torch.distributed.get_world_size())
|
|
else:
|
|
self._dali_data_iterator = self._init_iterator(
|
|
gpu_id=gpu_id,
|
|
dataset_path=dataset_path,
|
|
config_data=config_data,
|
|
config_features=config_features,
|
|
json_names=json_names,
|
|
symbols=symbols,
|
|
train_pipeline=pipeline_type == "train")
|
|
|
|
def _init_iterator(self, gpu_id, dataset_path, config_data,
|
|
config_features, json_names: list, symbols: list,
|
|
train_pipeline: bool):
|
|
"""Returns an iterator over data preprocessed with Dali."""
|
|
|
|
def hash_list_of_strings(li):
|
|
return str(abs(hash(''.join(li))))
|
|
|
|
output_files, transcripts = {}, {}
|
|
max_duration = config_data['max_duration']
|
|
for jname in json_names:
|
|
of, tr = _parse_json(
|
|
jname if jname[0] == '/' else os.path.join(dataset_path, jname),
|
|
len(output_files),
|
|
predicate=lambda json: json['original_duration'] <= max_duration)
|
|
output_files.update(of)
|
|
transcripts.update(tr)
|
|
file_list_path = os.path.join(
|
|
"/tmp", "asr_dali.file_list." + hash_list_of_strings(json_names))
|
|
_dict_to_file(output_files, file_list_path)
|
|
self.dataset_size = len(output_files)
|
|
print_once('Dataset read by DALI. '
|
|
f'Number of samples: {self.dataset_size}')
|
|
|
|
pipeline = make_dali_asr_pipeline(
|
|
config_data=config_data,
|
|
config_features=config_features,
|
|
device_id=gpu_id,
|
|
file_root=dataset_path,
|
|
file_list=file_list_path,
|
|
device_type=self.device_type,
|
|
batch_size=self.batch_size,
|
|
train_pipeline=train_pipeline)
|
|
|
|
return DaliIterator([pipeline], transcripts=transcripts,
|
|
symbols=symbols, batch_size=self.batch_size,
|
|
reader_name="file_reader",
|
|
train_iterator=train_pipeline)
|
|
|
|
def _init_synth_iterator(self, batch_size, nfeatures, iters_per_epoch,
|
|
ngpus):
|
|
self.dataset_size = ngpus * iters_per_epoch * batch_size
|
|
return SyntheticDataIterator(batch_size, nfeatures, regenerate=True)
|
|
|
|
@staticmethod
|
|
def _parse_pipeline_type(pipeline_type):
|
|
pipe = pipeline_type.lower()
|
|
assert pipe in ("train", "val", "synth"), \
|
|
'Invalid pipeline type (choices: "train", "val", "synth").'
|
|
return pipe
|
|
|
|
def _shard_size(self):
|
|
"""
|
|
Total number of samples handled by a single GPU in a single epoch.
|
|
"""
|
|
world_size = dist.get_world_size() if dist.is_initialized() else 1
|
|
if self.drop_last:
|
|
divisor = world_size * self.batch_size * self.grad_accumulation_steps
|
|
return self.dataset_size // divisor * divisor // world_size
|
|
else:
|
|
return int(math.ceil(self.dataset_size / world_size))
|
|
|
|
def __len__(self):
|
|
"""
|
|
Number of batches handled by each GPU.
|
|
"""
|
|
if self.drop_last:
|
|
assert self._shard_size() % self.batch_size == 0, \
|
|
f'{self._shard_size()} {self.batch_size}'
|
|
|
|
return int(math.ceil(self._shard_size() / self.batch_size))
|
|
|
|
def data_iterator(self):
|
|
return self._dali_data_iterator
|
|
|
|
def __iter__(self):
|
|
return self._dali_data_iterator
|