steffen/masks_augments_compare-21
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fingerprinted now should work correctly

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Si11ium 2020-05-20 13:29:17 +02:00
parent 7dd10d9a14
commit e021e2209b
6 changed files with 28 additions and 39 deletions
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6
_paramters.py
View File

@ -21,18 +21,16 @@ main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
main_arg_parser.add_argument("--data_worker", type=int, default=11, help="")
main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
main_arg_parser.add_argument("--data_class_name", type=str, default='BinaryMasksDataset', help="")
main_arg_parser.add_argument("--data_normalized", type=strtobool, default=True, help="")
main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--data_n_mels", type=int, default=64, help="")
main_arg_parser.add_argument("--data_sr", type=int, default=16000, help="")
main_arg_parser.add_argument("--data_hop_length", type=int, default=256, help="")
main_arg_parser.add_argument("--data_n_fft", type=int, default=512, help="")
main_arg_parser.add_argument("--data_mixup", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--data_stretch", type=strtobool, default=True, help="")
# Transformation Parameters
main_arg_parser.add_argument("--data_loudness_ratio", type=float, default=0, help="") # 0.4
main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0.3, help="") # 0.4
main_arg_parser.add_argument("--data_shift_ratio", type=float, default=0.3, help="") # 0.3
main_arg_parser.add_argument("--data_noise_ratio", type=float, default=0, help="") # 0.4
main_arg_parser.add_argument("--data_mask_ratio", type=float, default=0, help="") # 0.2
main_arg_parser.add_argument("--data_speed_ratio", type=float, default=0, help="") # 0.3
@ -54,7 +52,7 @@ main_arg_parser.add_argument("--train_outpath", type=str, default="output", help
main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
# FIXME: Stochastic weight Avaraging is not good, maybe its my implementation?
main_arg_parser.add_argument("--train_sto_weight_avg", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--train_weight_decay", type=float, default=1e-8, help="")
main_arg_parser.add_argument("--train_weight_decay", type=float, default=1e-7, help="")
main_arg_parser.add_argument("--train_opt_reset_interval", type=int, default=0, help="")
main_arg_parser.add_argument("--train_epochs", type=int, default=51, help="")
main_arg_parser.add_argument("--train_batch_size", type=int, default=300, help="")

35
datasets/binar_masks.py
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@ -1,7 +1,6 @@
import pickle
from collections import defaultdict
from pathlib import Path
import random
import librosa as librosa
from torch.utils.data import Dataset
@ -19,7 +18,7 @@ class BinaryMasksDataset(Dataset):
def sample_shape(self):
return self[0][0].shape
def __init__(self, data_root, setting, mel_transforms, transforms=None, mixup=False, stretch_dataset=False,
def __init__(self, data_root, setting, mel_transforms, transforms=None, stretch_dataset=False,
use_preprocessed=True):
self.use_preprocessed = use_preprocessed
self.stretch = stretch_dataset
@ -29,7 +28,6 @@ class BinaryMasksDataset(Dataset):
self.data_root = Path(data_root)
self.setting = setting
self.mixup = mixup
self._wav_folder = self.data_root / 'wav'
self._mel_folder = self.data_root / 'mel'
self.container_ext = '.pik'
@ -40,19 +38,20 @@ class BinaryMasksDataset(Dataset):
self._transforms = transforms or F_x(in_shape=None)
def _build_labels(self):
labeldict = dict()
with open(Path(self.data_root) / 'lab' / 'labels.csv', mode='r') as f:
# Exclude the header
_ = next(f)
labeldict = dict()
for row in f:
if self.setting not in row:
continue
filename, label = row.strip().split(',')
labeldict[filename] = self._to_label[label.lower()] if not self.setting == 'test' else filename
if self.stretch and self.setting == V.DATA_OPTIONS.train:
additional_dict = ({f'X_{key}': val for key, val in labeldict.items()})
additional_dict.update({f'X_X_{key}': val for key, val in labeldict.items()})
additional_dict.update({f'X_X_X_{key}': val for key, val in labeldict.items()})
additional_dict = ({f'X{key}': val for key, val in labeldict.items()})
additional_dict.update({f'XX{key}': val for key, val in labeldict.items()})
additional_dict.update({f'XXX{key}': val for key, val in labeldict.items()})
additional_dict.update({f'XXXX{key}': val for key, val in labeldict.items()})
labeldict.update(additional_dict)
# Delete File if one exists.
@ -66,12 +65,12 @@ class BinaryMasksDataset(Dataset):
return labeldict
def __len__(self):
return len(self._labels) * 2 if self.mixup else len(self._labels)
return len(self._labels)
def _compute_or_retrieve(self, filename):
if not (self._mel_folder / (filename + self.container_ext)).exists():
raw_sample, sr = librosa.core.load(self._wav_folder / (filename.replace('X_', '') + '.wav'))
raw_sample, sr = librosa.core.load(self._wav_folder / (filename.replace('X', '') + '.wav'))
mel_sample = self._mel_transform(raw_sample)
self._mel_folder.mkdir(exist_ok=True, parents=True)
with (self._mel_folder / (filename + self.container_ext)).open(mode='wb') as f:
@ -82,28 +81,16 @@ class BinaryMasksDataset(Dataset):
return mel_sample
def __getitem__(self, item):
is_mixed = item >= len(self._labels)
if is_mixed:
item = item - len(self._labels)
key: str = list(self._labels.keys())[item]
filename = key.replace('.wav', '')
mel_sample = self._compute_or_retrieve(filename)
label = self._labels[key]
if is_mixed:
label_sec = -1
while label_sec != self._labels[key]:
key_sec = random.choice(list(self._labels.keys()))
label_sec = self._labels[key_sec]
# noinspection PyUnboundLocalVariable
filename_sec = key_sec[:-4]
mel_sample_sec = self._compute_or_retrieve(filename_sec)
mix_in_border = int(random.random() * mel_sample.shape[-1]) * random.choice([1, -1])
mel_sample[:, :mix_in_border] = mel_sample_sec[:, :mix_in_border]
transformed_samples = self._transforms(mel_sample)
if not self.setting == 'test':
if self.setting != V.DATA_OPTIONS.test:
# In test, filenames instead of labels are returned. This is a little hacky though.
label = torch.as_tensor(label, dtype=torch.float)
return transformed_samples, label

11
main.py
View File

@ -110,6 +110,7 @@ def run_lightning_loop(config_obj):
inference_out = f'{parameters}_test_out.csv'
from main_inference import prepare_dataloader
import variables as V
test_dataloader = prepare_dataloader(config_obj)
with (outpath / model_type / parameters / version / inference_out).open(mode='w') as outfile:
@ -118,12 +119,12 @@ def run_lightning_loop(config_obj):
from tqdm import tqdm
for batch in tqdm(test_dataloader, total=len(test_dataloader)):
batch_x, file_name = batch
batch_x = batch_x.unsqueeze(0).to(device='cuda' if model.on_gpu else 'cpu')
batch_x = batch_x.to(device='cuda' if model.on_gpu else 'cpu')
y = model(batch_x).main_out
prediction = (y.squeeze() >= 0.5).int().item()
import variables as V
prediction = 'clear' if prediction == V.CLEAR else 'mask'
outfile.write(f'{file_name},{prediction}\n')
predictions = (y >= 0.5).int()
for prediction in predictions:
prediction_text = 'clear' if prediction == V.CLEAR else 'mask'
outfile.write(f'{file_name},{prediction_text}\n')
return model

3
main_inference.py
View File

@ -43,7 +43,8 @@ def prepare_dataloader(config_obj):
mel_transforms=mel_transforms, transforms=transforms
)
# noinspection PyTypeChecker
return DataLoader(dataset, batch_size=None, num_workers=0, shuffle=False)
return DataLoader(dataset, batch_size=config_obj.train.batch_size,
num_workers=config_obj.data.worker, shuffle=False)
def restore_logger_and_model(log_dir):

4
multi_run.py
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@ -20,9 +20,9 @@ if __name__ == '__main__':
config = MConfig().read_namespace(args)
arg_dict = dict()
for seed in range(40, 45):
for seed in range(0, 10):
arg_dict.update(main_seed=seed)
for model in ['CC', 'BCMC', 'BCC', 'RCC']:
for model in ['CC']: # , 'BCMC', 'BCC', 'RCC']:
arg_dict.update(model_type=model)
raw_conf = dict(data_speed_factor=0.0, data_speed_ratio=0.0, data_mask_ratio=0.0,
data_noise_ratio=0.0, data_shift_ratio=0.0, data_loudness_ratio=0.0,

8
util/module_mixins.py
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@ -122,7 +122,8 @@ class BinaryMaskDatasetMixin:
mel_transforms = Compose([
# Audio to Mel Transformations
AudioToMel(sr=self.params.sr, n_mels=self.params.n_mels, n_fft=self.params.n_fft,
hop_length=self.params.hop_length), MelToImage()])
hop_length=self.params.hop_length),
MelToImage()])
# Data Augmentations
aug_transforms = Compose([
RandomApply([
@ -132,7 +133,8 @@ class BinaryMaskDatasetMixin:
MaskAug(self.params.mask_ratio),
], p=0.6),
# Utility
NormalizeLocal(), ToTensor()
NormalizeLocal(),
ToTensor()
])
val_transforms = Compose([NormalizeLocal(), ToTensor()])
@ -143,7 +145,7 @@ class BinaryMaskDatasetMixin:
# TRAIN DATASET
train_dataset=BinaryMasksDataset(self.params.root, setting=V.DATA_OPTIONS.train,
use_preprocessed=self.params.use_preprocessed,
mixup=self.params.mixup, stretch_dataset=self.params.stretch,
stretch_dataset=self.params.stretch,
mel_transforms=mel_transforms_train, transforms=aug_transforms),
# VALIDATION DATASET
val_train_dataset=BinaryMasksDataset(self.params.root, setting=V.DATA_OPTIONS.train,