CCS intergration dataloader

datasets/ccs_librosa_datamodule.py

@@ -0,0 +1,169 @@
+import multiprocessing as mp
+from collections import defaultdict
+from pathlib import Path
+
+from torch.utils.data import DataLoader, ConcatDataset, WeightedRandomSampler
+from torchvision.transforms import Compose, RandomApply
+
+from ml_lib.audio_toolset.audio_io import NormalizeLocal
+from ml_lib.audio_toolset.audio_to_mel_dataset import LibrosaAudioToMelDataset
+from ml_lib.audio_toolset.mel_augmentation import NoiseInjection, LoudnessManipulator, ShiftTime, MaskAug
+from ml_lib.utils._basedatamodule import _BaseDataModule, DATA_OPTION_test, DATA_OPTION_train, DATA_OPTION_devel
+from ml_lib.utils.equal_sampler import EqualSampler
+from ml_lib.utils.transforms import ToTensor
+
+data_options = [DATA_OPTION_test, DATA_OPTION_train, DATA_OPTION_devel]
+
+
+class CCSLibrosaDatamodule(_BaseDataModule):
+
+    @property
+    def class_names(self):
+        return {key: val for val, key in enumerate(['negative', 'positive'])}
+
+    @property
+    def n_classes(self):
+        return len(self.class_names)
+
+    @property
+    def shape(self):
+        return self.datasets[DATA_OPTION_train].datasets[0][0][1].shape
+
+    @property
+    def mel_folder(self):
+        return self.root / 'mel_folder'
+
+    @property
+    def wav_folder(self):
+        return self.root / 'wav'
+
+    def __init__(self, data_root, batch_size, num_worker, sr, n_mels, n_fft, hop_length, sampler=None,
+                 random_apply_chance=0.5, target_mel_length_in_seconds=1,
+                 loudness_ratio=0.3, shift_ratio=0.3, noise_ratio=0.3, mask_ratio=0.3):
+        super(CCSLibrosaDatamodule, self).__init__()
+        self.sampler = sampler
+        self.samplers = None
+
+        self.num_worker = num_worker or 1
+        self.batch_size = batch_size
+        self.root = Path(data_root) / 'ComParE2021_CCS'
+        self.mel_length_in_seconds = target_mel_length_in_seconds
+
+        # Mel Transforms - will be pushed with all other paramters by self.__dict__ to subdataset-class
+        self.mel_kwargs = dict(sr=sr, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length)
+
+        # Utility
+        self.utility_transforms = Compose([NormalizeLocal(), ToTensor()])
+
+        # Data Augmentations
+        self.random_apply_chance = random_apply_chance
+        self.mel_augmentations = Compose([
+            RandomApply([NoiseInjection(noise_ratio)], p=random_apply_chance),
+            RandomApply([LoudnessManipulator(loudness_ratio)], p=random_apply_chance),
+            RandomApply([ShiftTime(shift_ratio)], p=random_apply_chance),
+            RandomApply([MaskAug(mask_ratio)], p=random_apply_chance),
+            self.utility_transforms])
+
+    def train_dataloader(self):
+        return DataLoader(dataset=self.datasets[DATA_OPTION_train], num_workers=self.num_worker, pin_memory=True,
+                          sampler=self.samplers[DATA_OPTION_train], batch_size=self.batch_size)
+
+    # Validation Dataloader
+    def val_dataloader(self):
+        return DataLoader(dataset=self.datasets[DATA_OPTION_devel], shuffle=False,
+                          batch_size=self.batch_size, pin_memory=False,
+                          num_workers=self.num_worker)
+
+    # Test Dataloader
+    def test_dataloader(self):
+        return DataLoader(dataset=self.datasets[DATA_OPTION_test], shuffle=False,
+                          batch_size=self.batch_size, pin_memory=False,
+                          num_workers=self.num_worker)
+
+    def _build_subdataset(self, row, build=False):
+        slice_file_name, class_name = row.strip().split(',')
+        class_id = self.class_names.get(class_name, -1)
+        audio_file_path = self.wav_folder / slice_file_name
+
+        # DATA OPTION DIFFERENTIATION !!!!!!!!!!! - Begin
+        kwargs = self.__dict__
+        if any([x in slice_file_name for x in [DATA_OPTION_devel, DATA_OPTION_test]]):
+            kwargs.update(mel_augmentations=self.utility_transforms)
+        # DATA OPTION DIFFERENTIATION !!!!!!!!!!! - End
+
+        target_frames = self.mel_length_in_seconds * self.mel_kwargs['sr']
+        sample_segment_length = target_frames // self.mel_kwargs['hop_length'] + 1
+        kwargs.update(sample_segment_len=sample_segment_length, sample_hop_len=sample_segment_length//2)
+        mel_dataset = LibrosaAudioToMelDataset(audio_file_path, class_id, **kwargs)
+        if build:
+            assert mel_dataset.build_mel()
+        return mel_dataset, class_id, slice_file_name
+
+    def prepare_data(self, *args, **kwargs):
+        datasets = dict()
+        for data_option in data_options:
+            with open(Path(self.root) / 'lab' / f'{data_option}.csv', mode='r') as f:
+                # Exclude the header
+                _ = next(f)
+                all_rows = list(f)
+            chunksize = len(all_rows) // max(self.num_worker, 1)
+            dataset = list()
+            with mp.Pool(processes=self.num_worker) as pool:
+
+                from itertools import repeat
+                results = pool.starmap_async(self._build_subdataset, zip(all_rows, repeat(True, len(all_rows))),
+                                             chunksize=chunksize)
+                for sub_dataset in results.get():
+                    dataset.append(sub_dataset[0])
+            datasets[data_option] = ConcatDataset(dataset)
+        self.datasets = datasets
+        return datasets
+
+    def setup(self, stag=None):
+        datasets = dict()
+        samplers = dict()
+        weights = dict()
+
+        for data_option in data_options:
+            with open(Path(self.root) / 'lab' / f'{data_option}.csv', mode='r') as f:
+                # Exclude the header
+                _ = next(f)
+                all_rows = list(f)
+            dataset = list()
+            for row in all_rows:
+                mel_dataset, class_id, _ = self._build_subdataset(row)
+                dataset.append(mel_dataset)
+            datasets[data_option] = ConcatDataset(dataset)
+
+            # Build Weighted Sampler for train and val
+            if data_option in [DATA_OPTION_train]:
+                if self.sampler == EqualSampler.__name__:
+                    class_idxs = [[idx for idx, (_, __, label) in enumerate(datasets[data_option]) if label == class_idx]
+                                  for class_idx in range(len(self.class_names))
+                                  ]
+                    samplers[data_option] = EqualSampler(class_idxs)
+                elif self.sampler == WeightedRandomSampler.__name__:
+                    class_counts = defaultdict(lambda: 0)
+                    for _, __, label in datasets[data_option]:
+                        class_counts[label] += 1
+                    len_largest_class = max(class_counts.values())
+
+                    weights[data_option] = [1 / class_counts[x] for x in range(len(class_counts))]
+                    ##############################################################################
+                    weights[data_option] = [weights[data_option][datasets[data_option][i][-1]]
+                                            for i in range(len(datasets[data_option]))]
+                    samplers[data_option] = WeightedRandomSampler(weights[data_option],
+                                                                  len_largest_class * len(self.class_names))
+                else:
+                    samplers[data_option] = None
+        self.datasets = datasets
+        self.samplers = samplers
+        return datasets
+
+    def purge(self):
+        import shutil
+
+        shutil.rmtree(self.mel_folder, ignore_errors=True)
+        print('Mel Folder has been recursively deleted')
+        print(f'Folder still exists: {self.mel_folder.exists()}')
+        return not self.mel_folder.exists()

datasets/primates_librosa_datamodule.py

@@ -17,7 +17,13 @@ data_options = [DATA_OPTION_test, DATA_OPTION_train, DATA_OPTION_devel]
 
 class PrimatesLibrosaDatamodule(_BaseDataModule):
 
-    class_names = {key: val for val, key in enumerate(['background', 'chimpanze', 'geunon', 'mandrille', 'redcap'])}
+    @property
+    def class_names(self):
+        return {key: val for val, key in enumerate(['background', 'chimpanze', 'geunon', 'mandrille', 'redcap'])}
+
+    @property
+    def n_classes(self):
+        return len(self.class_names)
 
     @property
     def shape(self):
@@ -33,19 +39,16 @@ class PrimatesLibrosaDatamodule(_BaseDataModule):
         return self.root / 'wav'
 
     def __init__(self, data_root, batch_size, num_worker, sr, n_mels, n_fft, hop_length, sampler=None,
-                 sample_segment_len=40, sample_hop_len=15, random_apply_chance=0.5,
+                 target_mel_length_in_seconds=0.7, random_apply_chance=0.5,
                  loudness_ratio=0.3, shift_ratio=0.3, noise_ratio=0.3, mask_ratio=0.3):
         super(PrimatesLibrosaDatamodule, self).__init__()
         self.sampler = sampler
         self.samplers = None
 
-        self.sample_hop_len = sample_hop_len
-        self.sample_segment_len = sample_segment_len
-
         self.num_worker = num_worker or 1
         self.batch_size = batch_size
         self.root = Path(data_root) / 'primates'
-        self.mel_length_in_seconds = 0.7
+        self.target_mel_length_in_seconds = target_mel_length_in_seconds
 
         # Mel Transforms - will be pushed with all other paramters by self.__dict__ to subdataset-class
         self.mel_kwargs = dict(sr=sr, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length)
@@ -89,7 +92,7 @@ class PrimatesLibrosaDatamodule(_BaseDataModule):
             kwargs.update(mel_augmentations=self.utility_transforms)
         # DATA OPTION DIFFERENTIATION !!!!!!!!!!! - End
 
-        target_frames = self.mel_length_in_seconds * self.mel_kwargs['sr']
+        target_frames = self.target_mel_length_in_seconds * self.mel_kwargs['sr']
         sample_segment_length = target_frames // self.mel_kwargs['hop_length'] + 1
         kwargs.update(sample_segment_len=sample_segment_length, sample_hop_len=sample_segment_length//2)
         mel_dataset = LibrosaAudioToMelDataset(audio_file_path, class_id, **kwargs)

main.py

@@ -60,7 +60,7 @@ def run_lightning_loop(h_params, data_class, model_class, seed=69, additional_ca
         trainer = Trainer.from_argparse_args(h_params, logger=logger, callbacks=callbacks)
 
         # Let Model pull what it wants
-        model = model_class.from_argparse_args(h_params, in_shape=datamodule.shape, n_classes=v.N_CLASS_multi)
+        model = model_class.from_argparse_args(h_params, in_shape=datamodule.shape, n_classes=datamodule.n_classes)
         model.init_weights()
 
         # trainer.test(model=model, datamodule=datamodule)

models/transformer_model.py

@@ -10,7 +10,7 @@ from einops import rearrange, repeat
 
 from ml_lib.metrics.multi_class_classification import MultiClassScores
 from ml_lib.modules.blocks import TransformerModule
-from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape, F_x)
+from ml_lib.modules.util import (LightningBaseModule, AutoPadToShape)
 from util.module_mixins import CombinedModelMixins
 
 MIN_NUM_PATCHES = 16
@@ -25,7 +25,7 @@ class VisualTransformer(CombinedModelMixins,
                  use_bias, use_norm, dropout, lat_dim, loss, scheduler, mlp_dim, head_dim,
                  lr, weight_decay, sto_weight_avg, lr_scheduler_parameter, opt_reset_interval):
 
-        # TODO: Move this to parent class, or make it much easieer to access... But How...
+        # TODO: Move this to parent class, or make it much easier to access... But How...
         a = dict(locals())
         params = {arg: a[arg] for arg in inspect.signature(self.__init__).parameters.keys() if arg != 'self'}
         super(VisualTransformer, self).__init__(params)
@@ -75,7 +75,7 @@ class VisualTransformer(CombinedModelMixins,
             nn.Linear(self.embed_dim, self.params.lat_dim),
             nn.GELU(),
             nn.Dropout(self.params.dropout),
-            nn.Linear(self.params.lat_dim, n_classes),
+            nn.Linear(self.params.lat_dim, self.params.n_classes),
             nn.Softmax()
         )
 
@@ -88,7 +88,7 @@ class VisualTransformer(CombinedModelMixins,
         tensor = self.autopad(x)
         p = self.params.patch_size
 
-        tensor = rearrange(tensor, 'b c (h p1) (w p2) -> b (w h) (p1 p2 c)', p1=p, p2=p)
+        tensor = rearrange(tensor, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
         b, n, _ = tensor.shape
 
         # mask
@@ -96,7 +96,7 @@ class VisualTransformer(CombinedModelMixins,
         mask = (lengths == torch.zeros_like(lengths))
         # CLS-token awareness
         # mask = torch.cat((torch.zeros(b, 1), mask), dim=-1)
-        # mask = repeat(mask, 'b n -> b n', h=self.params.heads)
+        # mask = repeat(mask, 'b n -> b h n', h=self.params.heads)
 
         tensor = self.patch_to_embedding(tensor)
 

multi_run.py

@@ -10,26 +10,27 @@ import itertools
 if __name__ == '__main__':
 
     # Set new values
-    hparams_dict = dict(seed=range(10),
+    hparams_dict = dict(seed=[69],
                         model_name=['VisualTransformer'],
-                        batch_size=[50],
-                        max_epochs=[250],
-                        random_apply_chance=[0.3],  # trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1),
-                        loudness_ratio=[0],  # trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1),
+                        data_name=['CCSLibrosaDatamodule'],
+                        batch_size=[5],
+                        max_epochs=[200],
+                        random_apply_chance=[0.5],  # trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1),
+                        loudness_ratio=[0.3],  # trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1),
                         shift_ratio=[0.3],  # trial.suggest_float('shift_ratio', 0.0, 0.5, step=0.1),
                         noise_ratio=[0.3],  # trial.suggest_float('noise_ratio', 0.0, 0.5, step=0.1),
                         mask_ratio=[0.3],  # trial.suggest_float('mask_ratio', 0.0, 0.5, step=0.1),
-                        lr=[5e-3],  # trial.suggest_uniform('lr', 1e-3, 3e-3),
+                        lr=[1e-2],  # trial.suggest_uniform('lr', 1e-3, 3e-3),
                         dropout=[0.2],  # trial.suggest_float('dropout', 0.0, 0.3, step=0.05),
-                        lat_dim=[32],  # 2 ** trial.suggest_int('lat_dim', 1, 5, step=1),
-                        mlp_dim=[16],  # 2 ** trial.suggest_int('mlp_dim', 1, 5, step=1),
-                        head_dim=[6],  # 2 ** trial.suggest_int('head_dim', 1, 5, step=1),
+                        lat_dim=[48],  # 2 ** trial.suggest_int('lat_dim', 1, 5, step=1),
+                        mlp_dim=[30],  # 2 ** trial.suggest_int('mlp_dim', 1, 5, step=1),
+                        head_dim=[12],  # 2 ** trial.suggest_int('head_dim', 1, 5, step=1),
                         patch_size=[12],  # trial.suggest_int('patch_size', 6, 12, step=3),
-                        attn_depth=[10],  # trial.suggest_int('attn_depth', 2, 14, step=4),
-                        heads=[6],  # trial.suggest_int('heads', 2, 16, step=2),
-                        scheduler=['CosineAnnealingWarmRestarts'],  # trial.suggest_categorical('scheduler', [None, 'LambdaLR']),
-                        lr_scheduler_parameter=[25],  # [0.98],
-                        embedding_size=[30],  # trial.suggest_int('embedding_size', 12, 64, step=12),
+                        attn_depth=[12],  # trial.suggest_int('attn_depth', 2, 14, step=4),
+                        heads=[12],  # trial.suggest_int('heads', 2, 16, step=2),
+                        scheduler=['LambdaLR'],  # trial.suggest_categorical('scheduler', [None, 'LambdaLR']),
+                        lr_scheduler_parameter=[0.95],  # [0.98],
+                        embedding_size=[64],  # trial.suggest_int('embedding_size', 12, 64, step=12),
                         loss=['ce_loss'],
                         sampler=['WeightedRandomSampler'],
                         # rial.suggest_categorical('sampler', [None, 'WeightedRandomSampler']),
@@ -40,7 +41,7 @@ if __name__ == '__main__':
     permutations_dicts = [dict(zip(keys, v)) for v in itertools.product(*values)]
     for permutations_dict in tqdm(permutations_dicts, total=len(permutations_dicts)):
         # Parse comandline args, read config and get model
-        cmd_args, found_data_class, found_model_class = parse_comandline_args_add_defaults(
+        cmd_args, *data_model_seed = parse_comandline_args_add_defaults(
             '_parameters.ini', overrides=permutations_dict)
 
         hparams = dict(**cmd_args)
@@ -50,6 +51,6 @@ if __name__ == '__main__':
         # RUN
         # ---------------------------------------
         print(f'Running Loop, parameters are: {permutations_dict}')
-        run_lightning_loop(hparams, found_data_class, found_model_class)
+        run_lightning_loop(hparams, *data_model_seed)
         print(f'Done, parameters were: {permutations_dict}')
         pass

rebuild_dataset.py

@@ -22,7 +22,7 @@ def rebuild_dataset(h_params, data_class):
 
 if __name__ == '__main__':
     # Parse comandline args, read config and get model
-    cmd_args, found_data_class, _ = parse_comandline_args_add_defaults('_parameters.ini')
+    cmd_args, found_data_class, _, _ = parse_comandline_args_add_defaults('_parameters.ini')
 
     # To NameSpace
     hparams = Namespace(**cmd_args)

util/module_mixins.py

@@ -19,7 +19,7 @@ class TrainMixin:
         y = self(batch_x).main_out
 
         if self.params.loss == 'focal_loss_rob':
-            labels_one_hot = torch.nn.functional.one_hot(batch_y, num_classes=5)
+            labels_one_hot = torch.nn.functional.one_hot(batch_y, num_classes=self.params.n_classes)
             loss = self.__getattribute__(self.params.loss)(y, labels_one_hot)
         else:
             loss = self.__getattribute__(self.params.loss)(y, batch_y.long())
@@ -58,7 +58,7 @@ class ValMixin:
         y_max = torch.stack(
             [torch.argmax(x.mean(dim=0)) if x.shape[0] > 1 else torch.argmax(x) for x in sorted_y.values()]
         ).squeeze()
-        y_one_hot = torch.nn.functional.one_hot(y_max, num_classes=5).float()
+        y_one_hot = torch.nn.functional.one_hot(y_max, num_classes=self.params.n_classes).float()
         self.metrics.update(y_one_hot, torch.stack(tuple(sorted_batch_y.values())).long())
 
         val_loss = self.ce_loss(y, batch_y.long())
@@ -96,7 +96,7 @@ class ValMixin:
         y_max = torch.stack(
             [torch.argmax(x.mean(dim=0)) if x.shape[0] > 1 else torch.argmax(x) for x in sorted_y.values()]
         ).squeeze()
-        y_one_hot = torch.nn.functional.one_hot(y_max, num_classes=5).float()
+        y_one_hot = torch.nn.functional.one_hot(y_max, num_classes=self.params.n_classes).float()
         max_vote_loss = self.ce_loss(y_one_hot, sorted_batch_y)
         summary_dict.update(val_max_vote_loss=max_vote_loss)
 
@@ -145,7 +145,11 @@ class TestMixin:
         y_max = torch.stack(
             [torch.argmax(x.mean(dim=0)) if x.shape[0] > 1 else torch.argmax(x) for x in sorted_y.values()]
         ).squeeze().cpu()
-        class_names = {val: key for val, key in enumerate(['background', 'chimpanze', 'geunon', 'mandrille', 'redcap'])}
+        if self.params.n_classes == 5:
+            class_names = {val: key for val, key in
+                           enumerate(['background', 'chimpanze', 'geunon', 'mandrille', 'redcap'])}
+        elif self.params.n_classes == 2:
+            class_names = {val: key for val, key in ['negative', 'positive']}
 
         df = pd.DataFrame(data=dict(filename=[Path(x).name for x in sorted_y.keys()],
                                     prediction=y_max.cpu().numpy()))
@@ -154,7 +158,7 @@ class TestMixin:
             try:
                 result_file.unlink()
             except:
-                print('File allready existed')
+                print('File already existed')
                 pass
         with result_file.open(mode='wb') as csv_file:
             df.to_csv(index=False, path_or_buf=csv_file)

variables.py

@@ -4,5 +4,6 @@ from pathlib import Path
 sr = 16000
 
 PRIMATES_Root = Path(__file__).parent / 'data' / 'primates'
+CCS_Root = Path(__file__).parent / 'data' / 'ComParE2021_CCS'
 
 N_CLASS_multi = 5