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import ast
import json
import logging
import math
import os
import random
# import h5py
from dataclasses import dataclass
from audioldm.clap.training.params import parse_args
# import braceexpand
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.datasets as datasets
import torchvision.transforms
# import webdataset as wds
from PIL import Image
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torch.utils.data.distributed import DistributedSampler
from functools import partial
import soundfile as sf
import io
from pathlib import Path
# import wget
from audioldm.clap.open_clip.utils import (
get_tar_path_from_dataset_name,
dataset_split,
)
from audioldm.clap.open_clip.utils import load_p, load_class_label
import copy
try:
import horovod.torch as hvd
except ImportError:
hvd = None
try:
import torchaudio
except ImportError:
torchaudio = None
from audioldm.clap.open_clip import tokenize
def tokenizer(text):
return tokenize(text).squeeze(0)
from transformers import RobertaTokenizer
tokenize = RobertaTokenizer.from_pretrained("roberta-base")
def tokenizer(text):
result = tokenize(
text,
padding="max_length",
truncation=True,
max_length=77,
return_tensors="pt",
)
return {k: v.squeeze(0) for k, v in result.items()}
# initizlied the audioset map
_AUDIOSET_MAP_PATH = os.path.join(Path(__file__).parent, "audioset_textmap.npy")
_AUDIOSET_MAP = np.load(_AUDIOSET_MAP_PATH, allow_pickle=True)
def int16_to_float32(x):
return (x / 32767.0).astype(np.float32)
def float32_to_int16(x):
x = np.clip(x, a_min=-1.0, a_max=1.0)
return (x * 32767.0).astype(np.int16)
# For Toy Dataset
# class ToyDataset(Dataset):
# def __init__(self, index_path, ipc, config, eval_mode=False):
# """Toy Dataset for testing the audioset input with text labels
# Parameters
# ----------
# index_path: str
# the link to the h5 file of each audio
# idc: str
# the link to the npy file, the number of samples in each class
# config: dict
# the audio cfg file
# eval_model (bool): to indicate if the dataset is a testing dataset
# """
# self.audio_cfg = config["audio_cfg"]
# self.text_cfg = config["text_cfg"]
# self.fp = h5py.File(index_path, "r")
# self.ipc = np.load(ipc, allow_pickle=True)
# self.total_size = len(self.fp["audio_name"])
# self.classes_num = self.audio_cfg["class_num"]
# self.eval_mode = eval_mode
# if not eval_mode:
# self.generate_queue()
# else:
# self.queue = []
# for i in range(self.total_size):
# target = self.fp["target"][i]
# if np.sum(target) > 0:
# self.queue.append(i)
# self.total_size = len(self.queue)
# logging.info("total dataset size: %d" % (self.total_size))
# logging.info("class num: %d" % (self.classes_num))
# def time_shifting(self, x):
# frame_num = len(x)
# shift_len = random.randint(0, frame_num - 1)
# new_sample = np.concatenate([x[shift_len:], x[:shift_len]], axis=0)
# return new_sample
# def generate_queue(self):
# self.queue = []
# while len(self.queue) < self.total_size:
# class_set = [*range(self.classes_num)]
# random.shuffle(class_set)
# self.queue += [
# self.ipc[d][random.randint(0, len(self.ipc[d]) - 1)] for d in class_set
# ]
# self.queue = self.queue[: self.total_size]
# logging.info("queue regenerated:%s" % (self.queue[-5:]))
# def crop_wav(self, x):
# crop_size = self.audio_cfg["crop_size"]
# crop_pos = random.randint(0, len(x) - crop_size - 1)
# return x[crop_pos : crop_pos + crop_size]
# def prompt_text(self, target):
# events = _AUDIOSET_MAP[np.where(target > 0)]
# event_text = "The sounds of " + ", ".join(events[:-1]) + " and " + events[-1]
# text = tokenize(event_text)[0]
# return text
# def __getitem__(self, index):
# """Load waveform, text, and target of an audio clip
# Parameters
# ----------
# index: int
# the index number
# Return
# ------
# output: dict {
# "hdf5_path": str,
# "index_in_hdf5": int,
# "audio_name": str,
# "waveform": list (audio_length,),
# "target": list (class_num, ),
# "text": torch.tensor (context_length,)
# }
# the output dictionary
# """
# s_index = self.queue[index]
# audio_name = self.fp["audio_name"][s_index].decode()
# # Hardcode here CHANGE
# hdf5_path = (
# self.fp["hdf5_path"][s_index]
# .decode()
# .replace(
# "../workspace",
# "/home/la/kechen/Research/ke_zsasp/workspace",
# )
# )
# r_idx = self.fp["index_in_hdf5"][s_index]
# target = self.fp["target"][s_index].astype(np.float32)
# text = self.prompt_text(target)
# with h5py.File(hdf5_path, "r") as f:
# waveform = int16_to_float32(f["waveform"][r_idx])[
# : self.audio_cfg["clip_samples"]
# ]
# assert (
# len(waveform) == self.audio_cfg["clip_samples"]
# ), "The sample length is not match"
# # Time shift
# # if (self.config.enable_time_shift) and (not self.eval_mode):
# # waveform = self.time_shifting(waveform)
# # # Label Enhance
# # if (self.config.crop_size is not None) and (not self.eval_mode):
# # waveform = self.crop_wav(waveform)
# # # the label enhance rate is fixed 0.5
# # if (self.config.enable_label_enhance) and (not self.eval_mode) and random.random() < 0.5:
# # kidx = np.where(target)[0]
# # for k in kidx:
# # for add_key in self.class_map[k][1]:
# # target[add_key] = 1.0
# # if len(self.class_map[k][2]) > 0:
# # add_key = random.choice(self.class_map[k][2])
# # target[add_key] = 1.0
# # missing the text input
# mel_spec = get_mel(torch.from_numpy(waveform), self.audio_cfg)[None, :, :]
# mel_spec = (
# torch.cat(
# [mel_spec, mel_spec.clone(), mel_spec.clone(), mel_spec.clone()], dim=0
# )
# .cpu()
# .numpy()
# )
# longer = random.choice([True, False])
# if longer == False:
# mel_spec[1:, :, :] = 0.0
# data_dict = {
# "hdf5_path": hdf5_path,
# "index_in_hdf5": r_idx,
# "audio_name": audio_name,
# "waveform": waveform,
# "class_label": target,
# "text": text,
# "longer": longer,
# "mel_fusion": mel_spec,
# }
# return data_dict
# def __len__(self):
# return self.total_size
class CsvDataset(Dataset):
def __init__(self, input_filename, transforms, img_key, caption_key, sep="\t"):
logging.debug(f"Loading csv data from {input_filename}.")
df = pd.read_csv(input_filename, sep=sep)
self.images = df[img_key].tolist()
self.captions = df[caption_key].tolist()
self.transforms = transforms
logging.debug("Done loading data.")
def __len__(self):
return len(self.captions)
def __getitem__(self, idx):
images = self.transforms(Image.open(str(self.images[idx])))
texts = tokenize([str(self.captions[idx])])[0]
return images, texts
@dataclass
class DataInfo:
dataloader: DataLoader
sampler: DistributedSampler
def preprocess_txt(text):
return tokenize([str(text)])[0]
def get_dataset_size(shards, sizefilepath_=None, is_local=True):
if isinstance(shards, list):
size_list = []
for s in shards:
size_list.append(
get_dataset_size(s, sizefilepath_=sizefilepath_, is_local=is_local)[0]
)
else:
if not is_local:
for n in dataset_split.keys():
if n in shards.split("/"):
break
for s in dataset_split[n]:
if s in shards.split("/"):
break
sizefilepath_ = f"./json_files/{n}/{s}/sizes.json"
shards_list = list(braceexpand.braceexpand(shards))
dir_path = os.path.dirname(shards)
if sizefilepath_ is not None:
sizes = json.load(open(sizefilepath_, "r"))
total_size = sum(
[
int(sizes[os.path.basename(shard.replace(".tar -", ".tar"))])
for shard in shards_list
]
)
else:
sizes_filename = os.path.join(dir_path, "sizes.json")
len_filename = os.path.join(dir_path, "__len__")
if os.path.exists(sizes_filename):
sizes = json.load(open(sizes_filename, "r"))
total_size = sum(
[int(sizes[os.path.basename(shard)]) for shard in shards_list]
)
elif os.path.exists(len_filename):
# FIXME this used to be eval(open(...)) but that seemed rather unsafe
total_size = ast.literal_eval(open(len_filename, "r").read())
else:
raise Exception(
"Cannot find sizes file for dataset. Please specify the path to the file."
)
# total_size = None # num samples undefined
# some common dataset sizes (at time of authors last download)
# cc3m-train: 2905954
# cc12m: 10968539
# LAION-400m: 407332084
num_shards = len(shards_list)
if isinstance(shards, list):
return sum(size_list), len(shards)
else:
return total_size, num_shards
def get_imagenet(args, preprocess_fns, split):
assert split in ["train", "val", "v2"]
is_train = split == "train"
preprocess_train, preprocess_val = preprocess_fns
if split == "v2":
from imagenetv2_pytorch import ImageNetV2Dataset
dataset = ImageNetV2Dataset(location=args.imagenet_v2, transform=preprocess_val)
else:
if is_train:
data_path = args.imagenet_train
preprocess_fn = preprocess_train
else:
data_path = args.imagenet_val
preprocess_fn = preprocess_val
assert data_path
dataset = datasets.ImageFolder(data_path, transform=preprocess_fn)
if is_train:
idxs = np.zeros(len(dataset.targets))
target_array = np.array(dataset.targets)
k = 50
for c in range(1000):
m = target_array == c
n = len(idxs[m])
arr = np.zeros(n)
arr[:k] = 1
np.random.shuffle(arr)
idxs[m] = arr
idxs = idxs.astype("int")
sampler = SubsetRandomSampler(np.where(idxs)[0])
else:
sampler = None
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
num_workers=args.workers,
sampler=sampler,
)
return DataInfo(dataloader, sampler)
def count_samples(dataloader):
os.environ["WDS_EPOCH"] = "0"
n_elements, n_batches = 0, 0
for images, texts in dataloader:
n_batches += 1
n_elements += len(images)
assert len(images) == len(texts)
return n_elements, n_batches
def filter_no_caption(sample):
return "txt" in sample
def log_and_continue(exn):
"""Call in an exception handler to ignore any exception, isssue a warning, and continue."""
logging.warning(f"Handling webdataset error ({repr(exn)}). Ignoring.")
return True
_SHARD_SHUFFLE_SIZE = 2000
_SHARD_SHUFFLE_INITIAL = 500
_SAMPLE_SHUFFLE_SIZE = 5000
_SAMPLE_SHUFFLE_INITIAL = 1000
def sample_prop(sizefile, inputs, proportion, is_local=True):
"""
Sample a proportion of the data.
"""
file_path_dict = {
os.path.split(inputs[i])[1]: os.path.split(inputs[i])[0]
for i in range(len(inputs))
}
sampled_filepath_dict = {}
sampled_size_dict = {}
if not is_local:
if os.path.exists("sizes.json"):
os.remove("sizes.json")
wget.download(sizefile, "sizes.json")
sizefile = "sizes.json"
with open(sizefile, "r", encoding="UTF-8") as f:
load_dict = json.load(f)
L = int(len(file_path_dict) * proportion)
subkeys = random.sample(file_path_dict.keys(), L)
for k in subkeys:
sampled_size_dict[k] = load_dict[k]
sampled_filepath_dict[k] = file_path_dict[k]
return (
sum(sampled_size_dict.values()),
L,
[os.path.join(v, k) for k, v in sampled_filepath_dict.items()],
sampled_size_dict,
)
def get_mel(audio_data, audio_cfg):
# mel shape: (n_mels, T)
mel = torchaudio.transforms.MelSpectrogram(
sample_rate=audio_cfg["sample_rate"],
n_fft=audio_cfg["window_size"],
win_length=audio_cfg["window_size"],
hop_length=audio_cfg["hop_size"],
center=True,
pad_mode="reflect",
power=2.0,
norm=None,
onesided=True,
n_mels=64,
f_min=audio_cfg["fmin"],
f_max=audio_cfg["fmax"],
).to(audio_data.device)
mel = mel(audio_data)
# Align to librosa:
# librosa_melspec = librosa.feature.melspectrogram(
# waveform,
# sr=audio_cfg['sample_rate'],
# n_fft=audio_cfg['window_size'],
# hop_length=audio_cfg['hop_size'],
# win_length=audio_cfg['window_size'],
# center=True,
# pad_mode="reflect",
# power=2.0,
# n_mels=64,
# norm=None,
# htk=True,
# f_min=audio_cfg['fmin'],
# f_max=audio_cfg['fmax']
# )
# we use log mel spectrogram as input
mel = torchaudio.transforms.AmplitudeToDB(top_db=None)(mel)
return mel.T # (T, n_mels)
def get_audio_features(
sample, audio_data, max_len, data_truncating, data_filling, audio_cfg
):
"""
Calculate and add audio features to sample.
Sample: a dict containing all the data of current sample.
audio_data: a tensor of shape (T) containing audio data.
max_len: the maximum length of audio data.
data_truncating: the method of truncating data.
data_filling: the method of filling data.
audio_cfg: a dict containing audio configuration. Comes from model_cfg['audio_cfg'].
"""
with torch.no_grad():
if len(audio_data) > max_len:
if data_truncating == "rand_trunc":
longer = torch.tensor([True])
elif data_truncating == "fusion":
# fusion
mel = get_mel(audio_data, audio_cfg)
# split to three parts
chunk_frames = (
max_len // audio_cfg["hop_size"] + 1
) # the +1 related to how the spectrogram is computed
total_frames = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is
# larger than max_len but smaller than max_len+hop_size.
# In this case, we just use the whole audio.
mel_fusion = torch.stack([mel, mel, mel, mel], dim=0)
sample["mel_fusion"] = mel_fusion
longer = torch.tensor([False])
else:
ranges = np.array_split(
list(range(0, total_frames - chunk_frames + 1)), 3
)
# print('total_frames-chunk_frames:', total_frames-chunk_frames,
# 'len(audio_data):', len(audio_data),
# 'chunk_frames:', chunk_frames,
# 'total_frames:', total_frames)
if len(ranges[1]) == 0:
# if the audio is too short, we just use the first chunk
ranges[1] = [0]
if len(ranges[2]) == 0:
# if the audio is too short, we just use the first chunk
ranges[2] = [0]
# randomly choose index for each part
idx_front = np.random.choice(ranges[0])
idx_middle = np.random.choice(ranges[1])
idx_back = np.random.choice(ranges[2])
# select mel
mel_chunk_front = mel[idx_front : idx_front + chunk_frames, :]
mel_chunk_middle = mel[idx_middle : idx_middle + chunk_frames, :]
mel_chunk_back = mel[idx_back : idx_back + chunk_frames, :]
# shrink the mel
mel_shrink = torchvision.transforms.Resize(size=[chunk_frames, 64])(
mel[None]
)[0]
# logging.info(f"mel_shrink.shape: {mel_shrink.shape}")
# stack
mel_fusion = torch.stack(
[mel_chunk_front, mel_chunk_middle, mel_chunk_back, mel_shrink],
dim=0,
)
sample["mel_fusion"] = mel_fusion
longer = torch.tensor([True])
else:
raise NotImplementedError(
f"data_truncating {data_truncating} not implemented"
)
# random crop to max_len (for compatibility)
overflow = len(audio_data) - max_len
idx = np.random.randint(0, overflow + 1)
audio_data = audio_data[idx : idx + max_len]
else: # padding if too short
if len(audio_data) < max_len: # do nothing if equal
if data_filling == "repeatpad":
n_repeat = int(max_len / len(audio_data))
audio_data = audio_data.repeat(n_repeat)
# audio_data = audio_data.unsqueeze(0).unsqueeze(0).unsqueeze(0)
# audio_data = F.interpolate(audio_data,size=max_len,mode="bicubic")[0,0,0]
audio_data = F.pad(
audio_data,
(0, max_len - len(audio_data)),
mode="constant",
value=0,
)
elif data_filling == "pad":
audio_data = F.pad(
audio_data,
(0, max_len - len(audio_data)),
mode="constant",
value=0,
)
elif data_filling == "repeat":
n_repeat = int(max_len / len(audio_data))
audio_data = audio_data.repeat(n_repeat + 1)[:max_len]
else:
raise NotImplementedError(
f"data_filling {data_filling} not implemented"
)
if data_truncating == "fusion":
mel = get_mel(audio_data, audio_cfg)
mel_fusion = torch.stack([mel, mel, mel, mel], dim=0)
sample["mel_fusion"] = mel_fusion
longer = torch.tensor([False])
sample["longer"] = longer
sample["waveform"] = audio_data
return sample
def preprocess(
sample,
audio_ext,
text_ext,
max_len,
audio_cfg,
class_index_dict=None,
data_filling="pad",
data_truncating="rand_trunc",
text_augment_selection=None,
):
"""
Preprocess a single sample for wdsdataloader.
"""
audio_data, orig_sr = sf.read(io.BytesIO(sample[audio_ext]))
audio_data = int16_to_float32(float32_to_int16(audio_data))
audio_data = torch.tensor(audio_data).float()
# TODO: (yusong) to be include in the future
# # if torchaudio not installed, use soundfile to load audio
# if torchaudio is None:
# audio_data, orig_sr = sf.read(io.BytesIO(sample[audio_ext]))
# audio_data = torch.tensor(audio_data).float()
# else:
# # https://github.com/webdataset/webdataset/blob/main/webdataset/autodecode.py
# with tempfile.TemporaryDirectory() as dirname:
# os.makedirs(dirname, exist_ok=True)
# fname = os.path.join(dirname, f"file.flac")
# with open(fname, "wb") as stream:
# stream.write(sample[audio_ext])
# audio_data, orig_sr = torchaudio.load(fname)
# audio_data = audio_data[0, :].float()
sample = get_audio_features(
sample, audio_data, max_len, data_truncating, data_filling, audio_cfg
)
del sample[audio_ext]
try:
json_dict_raw = json.loads(sample[text_ext].decode("utf-8"))
except:
print("sample[__url__]:", sample["__url__"])
# For selecting augmented text from dataset
if text_augment_selection is None or text_augment_selection == "none":
texts = json_dict_raw["text"]
elif text_augment_selection == "all":
if "text_augment_all" in json_dict_raw.keys():
texts = json_dict_raw["text_augment_all"]
else:
texts = json_dict_raw["text"]
elif text_augment_selection == "augment_only":
if "text_augment_all" in json_dict_raw.keys():
if json_dict_raw["text_augment_t5"] is None:
texts = json_dict_raw["text"]
else:
texts = json_dict_raw["text_augment_t5"]
else:
texts = json_dict_raw["text"]
else:
raise NotImplementedError(
f"text_augment_selection {text_augment_selection} not implemented"
)
sample["full_text"] = texts
if isinstance(texts, list) and isinstance(texts[0], str) and len(texts) > 1:
texts = random.choice(texts)
sample["raw_text"] = texts
sample["text"] = tokenizer(texts) # text shape: [num_token]
if class_index_dict is not None:
# https://stackoverflow.com/questions/48004243/how-to-share-large-read-only-dictionary-list-across-processes-in-multiprocessing
# https://stackoverflow.com/questions/45693949/storing-strings-in-a-multiprocessing-sharedctypes-array
# key, val = class_index_dict
# key = key[:].split('\n')
# _dict = {k: v for k, v in zip(key, val)}
sample["class_label"] = np.zeros(len(class_index_dict.keys()))
for x in json_dict_raw["tag"]:
sample["class_label"][class_index_dict[x]] = 1
sample["class_label"] = torch.tensor(sample["class_label"]).float()
del sample[text_ext]
sample["audio_name"] = sample["__key__"].split("/")[-1] + "." + audio_ext
sample["text_name"] = sample["__key__"].split("/")[-1] + "." + text_ext
sample["audio_orig_sr"] = orig_sr
return sample
def collate_fn(batch):
"""
Collate function for wdsdataloader.
batch: a list of dict, each dict is a sample
"""
# concatenate values in each dictionary. if it is a tensor, concatenate. if it is a list, extend.
batch_dict = {}
for k in batch[0].keys():
if isinstance(batch[0][k], dict): # dealwith bert tokenizer output
batch_dict[k] = {}
for kk in batch[0][k].keys():
tmp = []
for i in range(len(batch)):
tmp.append(batch[i][k][kk])
batch_dict[k][kk] = torch.vstack(tmp)
elif isinstance(batch[0][k], torch.Tensor):
batch_dict[k] = torch.stack([sample[k] for sample in batch])
elif isinstance(batch[0][k], np.ndarray):
batch_dict[k] = torch.tensor(np.stack([sample[k] for sample in batch]))
else:
batch_dict[k] = [sample[k] for sample in batch]
return batch_dict
def get_wds_dataset(
args,
model_cfg,
is_train,
audio_ext="flac",
text_ext="json",
max_len=480000,
proportion=1.0,
sizefilepath_=None,
is_local=None,
):
"""
Get a dataset for wdsdataloader.
"""
if is_local is None and (not args.remotedata is None):
is_local = not args.remotedata
input_shards = args.train_data if is_train else args.val_data
assert input_shards is not None
if not sizefilepath_ is None:
sizefilepath = sizefilepath_
else:
sizefilepath = os.path.join(os.path.dirname(input_shards[0]), "sizes.json")
if proportion != 1.0:
num_samples, num_shards, input_shards, _ = sample_prop(
sizefilepath, input_shards, proportion, is_local=is_local
)
else:
num_samples, num_shards = get_dataset_size(
input_shards, sizefilepath_=sizefilepath_, is_local=is_local
)
if not num_samples:
if is_train:
num_samples = args.train_num_samples
if not num_samples:
raise RuntimeError(
"Currently, number of dataset samples must be specified for training dataset. "
"Please specify via `--train-num-samples` if no dataset length info present."
)
else:
num_samples = (
args.val_num_samples or 0
) # eval will just exhaust the iterator if not specified
pipeline = [wds.SimpleShardList(input_shards)]
# at this point we have an iterator over all the shards
# TODO: (yusong): add a if statement of distributed. If not, we don't need to split_by_node
if is_train or args.parallel_eval:
pipeline.extend(
[
wds.detshuffle(
bufsize=_SHARD_SHUFFLE_SIZE,
initial=_SHARD_SHUFFLE_INITIAL,
seed=args.seed,
),
wds.split_by_node,
wds.split_by_worker,
# at this point, we have an iterator over the shards assigned to each worker at each node
wds.tarfile_to_samples(handler=log_and_continue),
wds.shuffle(
bufsize=_SAMPLE_SHUFFLE_SIZE,
initial=_SAMPLE_SHUFFLE_INITIAL,
rng=random.Random(args.seed),
),
# wds.repeatedly, # FIXME determine if this is beneficial
]
)
else:
pipeline.extend(
[
wds.split_by_worker,
# at this point, we have an iterator over the shards assigned to each worker
wds.tarfile_to_samples(handler=log_and_continue),
]
)
pipeline.append(
wds.map(
partial(
preprocess,
audio_ext=audio_ext,
text_ext=text_ext,
max_len=max_len,
audio_cfg=model_cfg["audio_cfg"],
class_index_dict=copy.deepcopy(args.class_index_dict),
data_filling=args.data_filling,
data_truncating=args.data_truncating,
text_augment_selection=args.text_augment_selection,
)
),
)
pipeline.append(
wds.batched(
args.batch_size,
partial=not (is_train or args.parallel_eval),
collation_fn=collate_fn,
)
)
dataset = wds.DataPipeline(*pipeline)
if is_train or args.parallel_eval:
# (yusong): Currently parallel evaluation will be not precise as we are repeat the last few samples.
# (yusong): See comments below.
# roll over and repeat a few samples to get same number of full batches on each node
global_batch_size = args.batch_size * args.world_size
num_batches = math.ceil(num_samples / global_batch_size)
num_workers = max(1, args.workers)
num_worker_batches = math.ceil(
num_batches / num_workers
) # per dataloader worker
num_batches = num_worker_batches * num_workers
num_samples = num_batches * global_batch_size
dataset = dataset.with_epoch(
num_worker_batches
) # each worker is iterating over this
else:
# last batches are partial, eval is done on single (master) node
num_batches = math.ceil(num_samples / args.batch_size)
kwargs = {}
if args.horovod: # multi-node training on summit
kwargs["multiprocessing_context"] = "forkserver"
dataloader = wds.WebLoader(
dataset, batch_size=None, shuffle=False, num_workers=args.workers, **kwargs
)
# FIXME not clear which approach is better, with_epoch before vs after dataloader?
# hoping to resolve via https://github.com/webdataset/webdataset/issues/169
# if is_train:
# # roll over and repeat a few samples to get same number of full batches on each node
# global_batch_size = args.batch_size * args.world_size
# num_batches = math.ceil(num_samples / global_batch_size)
# num_workers = max(1, args.workers)
# num_batches = math.ceil(num_batches / num_workers) * num_workers
# num_samples = num_batches * global_batch_size
# dataloader = dataloader.with_epoch(num_batches)
# else:
# # last batches are partial, eval is done on single (master) node
# num_batches = math.ceil(num_samples / args.batch_size)
# add meta-data to dataloader instance for convenience
dataloader.num_batches = num_batches
dataloader.num_samples = num_samples
return DataInfo(dataloader, None)
def wds_batch_list2dict(
batch,
keys=[
"__url__",
"__key__",
"waveform",
"text",
"raw_text",
"audio_name",
"text_name",
"audio_orig_sr",
],
):
"""
Return a dictionary of the batch, with keys as the names of the fields.
"""
assert len(keys) == len(
batch
), "batch must have same number of keys as keys argument"
return {keys[i]: batch[i] for i in range(len(batch))}
def get_csv_dataset(args, preprocess_fn, is_train):
input_filename = args.train_data if is_train else args.val_data
assert input_filename
dataset = CsvDataset(
input_filename,
preprocess_fn,
img_key=args.csv_img_key,
caption_key=args.csv_caption_key,
sep=args.csv_separator,
)
num_samples = len(dataset)
sampler = DistributedSampler(dataset) if args.distributed and is_train else None
shuffle = is_train and sampler is None
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=is_train,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader, sampler)
def get_toy_dataset(args, model_cfg, is_train):
index_path = args.train_data if is_train else args.val_data
ipc_path = args.train_ipc if is_train else args.val_ipc
assert index_path and ipc_path
eval_mode = not is_train
dataset = ToyDataset(index_path, ipc_path, model_cfg, eval_mode=eval_mode)
num_samples = len(dataset)
sampler = (
DistributedSampler(dataset, shuffle=False)
if args.distributed and is_train
else None
)
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
sampler=sampler,
drop_last=is_train,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader, sampler)
def get_dataset_fn(data_path, dataset_type):
if dataset_type == "webdataset":
return get_wds_dataset
elif dataset_type == "csv":
return get_csv_dataset
elif dataset_type == "auto":
ext = data_path.split(".")[-1]
if ext in ["csv", "tsv"]:
return get_csv_dataset
elif ext in ["tar"]:
return get_wds_dataset
else:
raise ValueError(
f"Tried to figure out dataset type, but failed for extention {ext}."
)
elif dataset_type == "toy":
return get_toy_dataset
else:
raise ValueError(f"Unsupported dataset type: {dataset_type}")
def get_data(args, model_cfg):
data = {}
args.class_index_dict = load_class_label(args.class_label_path)
if args.datasetinfos is None:
args.datasetinfos = ["train", "unbalanced_train", "balanced_train"]
if args.dataset_type == "webdataset":
args.train_data = get_tar_path_from_dataset_name(
args.datasetnames,
args.datasetinfos,
islocal=not args.remotedata,
proportion=args.dataset_proportion,
dataset_path=args.datasetpath,
full_dataset=args.full_train_dataset,
)
if args.full_train_dataset is None:
args.full_train_dataset = []
if args.exclude_eval_dataset is None:
args.exclude_eval_dataset = []
excluded_eval_datasets = args.full_train_dataset + args.exclude_eval_dataset
val_dataset_names = (
[n for n in args.datasetnames if n not in excluded_eval_datasets]
if excluded_eval_datasets
else args.datasetnames
)
args.val_dataset_names = val_dataset_names
args.val_data = get_tar_path_from_dataset_name(
val_dataset_names,
["valid", "test", "eval"],
islocal=not args.remotedata,
proportion=1,
dataset_path=args.datasetpath,
full_dataset=None,
)
if args.train_data:
data["train"] = get_dataset_fn(args.train_data, args.dataset_type)(
args, model_cfg, is_train=True
)
if args.val_data:
data["val"] = get_dataset_fn(args.val_data, args.dataset_type)(
args, model_cfg, is_train=False
)
return data