import ast import json import logging import math import os import random import h5py from dataclasses import dataclass import braceexpand import numpy as np import pandas as pd import torch 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 from pathlib import Path import wget import tempfile import copy from contextlib import suppress from clap_module.utils import get_tar_path_from_dataset_name, dataset_split from clap_module.utils import load_p, load_class_label from clap_module import tokenize as clip_tokenizer from transformers import BertTokenizer from transformers import RobertaTokenizer from transformers import BartTokenizer try: import horovod.torch as hvd except ImportError: hvd = None try: import torchaudio except ImportError: torchaudio = None bert_tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") roberta_tokenizer = RobertaTokenizer.from_pretrained("roberta-base") bart_tokenizer = BartTokenizer.from_pretrained("facebook/bart-base") def tokenizer(text, tmodel="roberta", max_length=77): """tokenizer for different models tmodel is default to roberta as it is the best model for our task max_length is default to 77 from the OpenAI CLIP parameters We assume text to be a single string, but it can also be a list of strings """ if tmodel == "transformer": return clip_tokenizer(text).squeeze(0) elif tmodel == "bert": result = bert_tokenizer( text, padding="max_length", truncation=True, max_length=max_length, return_tensors="pt", ) return {k: v.squeeze(0) for k, v in result.items()} elif tmodel == "roberta": result = roberta_tokenizer( text, padding="max_length", truncation=True, max_length=max_length, return_tensors="pt", ) return {k: v.squeeze(0) for k, v in result.items()} elif tmodel == "bart": result = bart_tokenizer( text, padding="max_length", truncation=True, max_length=max_length, 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('float32') def float32_to_int16(x): x = np.clip(x, a_min=-1., a_max=1.) return (x * 32767.).astype('int16') def int16_to_float32_torch(x): return (x / 32767.0).type(torch.float32) def float32_to_int16_torch(x): x = torch.clamp(x, min=-1., max=1.) return (x * 32767.).type(torch.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 = tokenizer(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('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 @dataclass class DataInfo: dataloader: DataLoader sampler: DistributedSampler 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( f"Cannot find sizes file for dataset {shards}. 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 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 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_tf = 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=audio_cfg['mel_bins'], f_min=audio_cfg['fmin'], f_max=audio_cfg['fmax'] ).to(audio_data.device) mel = mel_tf(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=audio_cfg['mel_bins'], # 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, require_grad=False): """ 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']. require_grad: whether to require gradient for audio data. This is useful when we want to apply gradient-based classifier-guidance. """ grad_fn = suppress if require_grad else torch.no_grad with grad_fn(): 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, audio_cfg['mel_bins']])(mel[None])[0] # logging.info(f"mel_shrink.shape: {mel_shrink.shape}") # stack mel_fusion = torch.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back], 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 select_text(json_dict_raw, text_augment_selection): # 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" ) return texts def preprocess_single( sample, audio_ext, text_ext, max_len, audio_cfg, tmodel, class_index_dict, data_filling, data_truncating, text_augment_selection, ): """ Preprocess a single sample for wdsdataloader. """ audio_data, orig_sr = sample[audio_ext] audio_data = int16_to_float32_torch(float32_to_int16_torch(audio_data[0])) sample = get_audio_features(sample, audio_data, max_len, data_truncating, data_filling, audio_cfg) del sample[audio_ext] json_dict_raw = sample[text_ext] texts = select_text(json_dict_raw, text_augment_selection) 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, tmodel=tmodel) # 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 # in case the re-written version is wrong, here is the old version: # 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() class_labels = np.zeros(len(class_index_dict)) class_labels[np.in1d(list(class_index_dict.keys()), json_dict_raw["tag"])] = 1 sample["class_label"] = torch.tensor(class_labels).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_with_preprocess(batch, audio_ext, text_ext, max_len, audio_cfg, args, ): """ Collate function for wdsdataloader. batch: a list of dict, each dict is a sample """ class_index_dict = copy.deepcopy(args.class_index_dict) # To avoid deadlock in multiprocessing data_filling = args.data_filling data_truncating = args.data_truncating text_augment_selection = args.text_augment_selection tmodel = args.tmodel # concatenate values in each dictionary. if it is a tensor, concatenate. if it is a list, extend. data_preprocessed = [] for sample in batch: data_preprocessed.append( preprocess_single(sample, audio_ext, text_ext, max_len, audio_cfg, tmodel, class_index_dict, data_filling, data_truncating, text_augment_selection)) batch_dict = {} for k in data_preprocessed[0].keys(): if isinstance(data_preprocessed[0][k], dict): # dealwith bert tokenizer output batch_dict[k] = {} for kk in data_preprocessed[0][k].keys(): tmp = [] for i in range(len(data_preprocessed)): tmp.append(data_preprocessed[i][k][kk]) batch_dict[k][kk] = torch.vstack(tmp) elif isinstance(data_preprocessed[0][k], torch.Tensor): batch_dict[k] = torch.stack([sample[k] for sample in data_preprocessed]) elif isinstance(data_preprocessed[0][k], np.ndarray): batch_dict[k] = torch.tensor(np.stack([sample[k] for sample in data_preprocessed])) else: batch_dict[k] = [sample[k] for sample in data_preprocessed] del data_preprocessed 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.decode(wds.torch_audio), ) pipeline.append( wds.batched( args.batch_size, partial=not (is_train or args.parallel_eval), collation_fn=partial(collate_fn_with_preprocess, audio_ext=audio_ext, text_ext=text_ext, max_len=max_len, audio_cfg=model_cfg['audio_cfg'], args=args, ), ) ) 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" if is_train: if args.prefetch_factor: prefetch_factor = args.prefetch_factor else: prefetch_factor = max(2, args.batch_size // args.workers) else: prefetch_factor = 2 dataloader = wds.WebLoader( dataset, batch_size=None, shuffle=False, num_workers=args.workers, pin_memory=True, prefetch_factor=prefetch_factor, **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_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(dataset_type): if dataset_type == "webdataset": return get_wds_dataset 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.dataset_type)( args, model_cfg, is_train=True ) if args.val_data: data["val"] = get_dataset_fn(args.dataset_type)( args, model_cfg, is_train=False ) return data