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Revert "add random sampler" 

This reverts commit aa52d1ed93.
This commit is contained in:
shahules786 2022-10-29 10:33:59 +05:30
parent ad208ca0a0
commit 6f1acf0423
1 changed files with 4 additions and 6 deletions
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10
enhancer/data/dataset.py
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@ -8,7 +8,7 @@ import numpy as np
import pytorch_lightning as pl import pytorch_lightning as pl
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, RandomSampler from torch.utils.data import DataLoader, Dataset
from torch_audiomentations import Compose from torch_audiomentations import Compose
from enhancer.data.fileprocessor import Fileprocessor from enhancer.data.fileprocessor import Fileprocessor
@ -135,7 +135,6 @@ class TaskDataset(pl.LightningDataModule):
speaker_index = rng.choice(possible_indices) speaker_index = rng.choice(possible_indices)
possible_indices.remove(speaker_index) possible_indices.remove(speaker_index)
speaker_name = all_speakers[speaker_index] speaker_name = all_speakers[speaker_index]
print(f"Selected f{speaker_name} for valid")
file_indices = [ file_indices = [
i i
for i, file in enumerate(data) for i, file in enumerate(data)
@ -223,13 +222,11 @@ class TaskDataset(pl.LightningDataModule):
return generator.manual_seed(seed) return generator.manual_seed(seed)
def train_dataloader(self): def train_dataloader(self):
dataset = TrainDataset(self)
sampler = RandomSampler(dataset, generator=self.generator)
return DataLoader( return DataLoader(
dataset, TrainDataset(self),
batch_size=self.batch_size, batch_size=self.batch_size,
num_workers=self.num_workers, num_workers=self.num_workers,
sampler=sampler, generator=self.generator,
collate_fn=self.train_collatefn, collate_fn=self.train_collatefn,
) )
@ -340,6 +337,7 @@ class EnhancerDataset(TaskDataset):
return self.prepare_segment(*self._test[idx]) return self.prepare_segment(*self._test[idx])
def prepare_segment(self, file_dict: dict, start_time: float): def prepare_segment(self, file_dict: dict, start_time: float):
clean_segment = self.audio( clean_segment = self.audio(
file_dict["clean"], offset=start_time, duration=self.duration file_dict["clean"], offset=start_time, duration=self.duration
) )