Code Comments, Getting Dirty Env, Naming

notebooks

.gitignore

@@ -1,6 +1 @@
-/.idea/
-# my own stuff
-
-/data
-/.idea
-/ml_lib
+.idea

README.md

@@ -9,9 +9,10 @@ Clone it to find a collection of:
  - Utility Function for Model I/O
  - DL Modules
  - A Plotter Object
- - Audio Related Tools and Funtion
+ - Audio related Tools and Funtion
    - Librosa
    - Scipy Signal
+ - PointCloud related Tools and Functions
  
  ###Notes:
  - Use directory links to link from your main project folder to the ml_lib folder. Pycharm will automatically use 
@@ -19,11 +20,10 @@ Clone it to find a collection of:
     \
     \
     For Windows Users:
-    ```
+    ``` bash
      mklink /d "ml_lib" "..\ml_lib""
     ```
     For Unix User:
+    ``` bash
+    ln -s ../ml_lib ml_lib
     ```
-    TBA
-    ```
-  - Cheers

_templates/new_project/.gitignore

@@ -0,0 +1,5 @@
+# my own stuff
+
+/data
+/.idea
+/ml_lib

_templates/new_project/datasets/template_dataset.py

@@ -2,5 +2,16 @@ from torch.utils.data import Dataset
 
 
 class TemplateDataset(Dataset):
+
+    @property
+    def sample_shape(self):
+        return self[0][0].shape
+
     def __init__(self, *args, **kwargs):
-        super(TemplateDataset, self).__init__()
+        super(TemplateDataset, self).__init__()
+
+    def __len__(self):
+        pass
+
+    def __getitem__(self, item):
+        return item

_templates/new_project/main.py

@@ -7,10 +7,9 @@ import torch
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
 
-from modules.utils import LightningBaseModule
-from utils.config import Config
-from utils.logging import Logger
-from utils.model_io import SavedLightningModels
+from ml_lib.modules.util import LightningBaseModule
+from ml_lib.utils.config import Config
+from ml_lib.utils.loggers import LightningLogger
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)
@@ -21,7 +20,7 @@ def run_lightning_loop(config_obj):
     # Logging
     # ================================================================================
     # Logger
-    with Logger(config_obj) as logger:
+    with LightningLogger(config_obj) as logger:
         # Callbacks
         # =============================================================================
         # Checkpoint Saving
@@ -44,11 +43,6 @@ def run_lightning_loop(config_obj):
         # Init
         model: LightningBaseModule = config_obj.model_class(config_obj.model_paramters)
         model.init_weights(torch.nn.init.xavier_normal_)
-        if model.name == 'CNNRouteGeneratorDiscriminated':
-            # ToDo: Make this dependent on the used seed
-            path = logger.outpath / 'classifier_cnn' / 'version_0'
-            disc_model = SavedLightningModels.load_checkpoint(path).restore()
-            model.set_discriminator(disc_model)
 
         # Trainer
         # =============================================================================
@@ -70,8 +64,8 @@ def run_lightning_loop(config_obj):
         trainer.fit(model)
 
         # Save the last state & all parameters
-        trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
-        model.save_to_disk(logger.log_dir)
+        trainer.save_checkpoint(config_obj.exp_path.log_dir / 'weights.ckpt')
+        model.save_to_disk(config_obj.exp_path)
 
         # Evaluate It
         if config_obj.main.eval:

_templates/new_project/multi_run.py

@@ -1,6 +1,6 @@
 import warnings
 
-from utils.config import Config
+from ml_lib._templates.new_project.utils.project_config import Config
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)
@@ -8,17 +8,16 @@ warnings.filterwarnings('ignore', category=UserWarning)
 # Imports
 # =============================================================================
 
-from _templates.new_project.main import run_lightning_loop, args
+from ml_lib._templates.new_project.main import run_lightning_loop, args
 
 
 if __name__ == '__main__':
 
     # Model Settings
     config = Config().read_namespace(args)
-    # bias, activation, model, norm, max_epochs, filters
-    cnn_classifier = dict(train_epochs=10, model_use_bias=True, model_use_norm=True, model_activation='leaky_relu',
-                          model_type='classifier_cnn', model_filters=[16, 32, 64], data_batchsize=512)
-    # bias, activation, model, norm, max_epochs, sr, feature_mixed_dim, filters
+    # bias, activation, model, norm, max_epochs
+    cnn_classifier = dict(train_epochs=10, model_use_bias=True, model_use_norm=True, data_batchsize=512)
+    # bias, activation, model, norm, max_epochs
 
     for arg_dict in [cnn_classifier]:
         for seed in range(5):

_templates/new_project/utils/module_mixins.py

@@ -11,13 +11,13 @@ from torch.utils.data import DataLoader
 from torchcontrib.optim import SWA
 from torchvision.transforms import Compose
 
-from _templates.new_project.datasets.template_dataset import TemplateDataset
+from ml_lib._templates.new_project.datasets.template_dataset import TemplateDataset
 
-from audio_toolset.audio_io import NormalizeLocal
-from modules.utils import LightningBaseModule
-from utils.transforms import ToTensor
+from ml_lib.audio_toolset.audio_io import NormalizeLocal
+from ml_lib.modules.util import LightningBaseModule
+from ml_lib.utils.transforms import ToTensor
 
-from _templates.new_project.utils.project_config import GlobalVar as GlobalVars
+from ml_lib._templates.new_project.utils.project_config import GlobalVar as GlobalVars
 
 
 class BaseOptimizerMixin:
@@ -61,10 +61,11 @@ class BaseTrainMixin:
         assert isinstance(self, LightningBaseModule)
         keys = list(outputs[0].keys())
 
-        summary_dict = dict(log={f'mean_{key}': torch.mean(torch.stack([output[key]
-                                                                        for output in outputs]))
-                                 for key in keys if 'loss' in key})
-        return summary_dict
+        summary_dict = {f'mean_{key}': torch.mean(torch.stack([output[key]
+                                                               for output in outputs]))
+                        for key in keys if 'loss' in key}
+        for key in summary_dict.keys():
+            self.log(key, summary_dict[key])
 
 
 class BaseValMixin:
@@ -83,16 +84,16 @@ class BaseValMixin:
 
     def validation_epoch_end(self, outputs, *_, **__):
         assert isinstance(self, LightningBaseModule)
-        summary_dict = dict(log=dict())
+        summary_dict = dict()
         # In case of Multiple given dataloader this will outputs will be: list[list[dict[]]]
         # for output_idx, output in enumerate(outputs):
         # else:list[dict[]]
         keys = list(outputs.keys())
         # Add Every Value das has a "loss" in it, by calc. mean over all occurences.
-        summary_dict['log'].update({f'mean_{key}': torch.mean(torch.stack([output[key]
-                                                                           for output in outputs]))
-                                    for key in keys if 'loss' in key}
-                                   )
+        summary_dict.update({f'mean_{key}': torch.mean(torch.stack([output[key]
+                                                                    for output in outputs]))
+                             for key in keys if 'loss' in key}
+                            )
         """
         # Additional Score like the unweighted Average Recall:
         # UnweightedAverageRecall
@@ -107,7 +108,8 @@ class BaseValMixin:
         summary_dict['log'].update({f'uar_score': uar_score})
         """
 
-        return summary_dict
+        for key in summary_dict.keys():
+            self.log(key, summary_dict[key])
 
 
 class BinaryMaskDatasetMixin:

_templates/new_project/utils/project_config.py

@@ -1,8 +1,5 @@
 from argparse import Namespace
 
-from utils.config import Config
-
-
 class GlobalVar(Namespace):
     # Labels for classes
     LEFT = 1
@@ -21,10 +18,3 @@ class GlobalVar(Namespace):
     train='train',
     vali='vali',
     test='test'
-
-
-class ThisConfig(Config):
-
-    @property
-    def _model_map(self):
-        return dict()

additions/losses.py

@@ -0,0 +1,75 @@
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class FocalLoss(nn.modules.loss._WeightedLoss):
+    def __init__(self, weight=None, gamma=2,reduction='mean'):
+        super(FocalLoss, self).__init__(weight,reduction=reduction)
+        self.gamma = gamma
+        self.weight = weight  # weight parameter will act as the alpha parameter to balance class weights
+
+    def forward(self, input, target):
+
+        ce_loss = F.cross_entropy(input, target, reduction=self.reduction, weight=self.weight)
+        pt = torch.exp(-ce_loss)
+        focal_loss = ((1 - pt) ** self.gamma * ce_loss).mean()
+        return focal_loss
+
+
+class FocalLossRob(nn.Module):
+    # taken from https://github.com/mathiaszinnen/focal_loss_torch/blob/main/focal_loss/focal_loss.py
+    def __init__(self, alpha=1, gamma=2, reduction: str = 'mean'):
+        super().__init__()
+        if reduction not in ['mean', 'none', 'sum']:
+            raise NotImplementedError('Reduction {} not implemented.'.format(reduction))
+        self.reduction = reduction
+        self.alpha = alpha
+        self.gamma = gamma
+
+    def forward(self, x, target):
+        x = x.clamp(1e-7, 1. - 1e-7)  # own addition
+        p_t = torch.where(target == 1, x, 1-x)
+        fl = - 1 * (1 - p_t) ** self.gamma * torch.log(p_t)
+        fl = torch.where(target == 1, fl * self.alpha, fl)
+        return self._reduce(fl)
+
+    def _reduce(self, x):
+        if self.reduction == 'mean':
+            return x.mean()
+        elif self.reduction == 'sum':
+            return x.sum()
+        else:
+            return x
+
+
+class DQN_MSELoss(object):
+
+    def __init__(self, agent_net, target_net, gamma):
+        self.agent_net = agent_net
+        self.target_net = target_net
+        self.gamma = gamma
+
+    def __call__(self, batch: Tuple[torch.Tensor, ...]) -> torch.Tensor:
+        """
+        Calculates the mse loss using a mini batch from the replay buffer
+        Args:
+            batch: current mini batch of replay data
+        Returns:
+            loss
+        """
+        states, actions, rewards, dones, next_states = batch
+
+        actions = actions.to(torch.int64)
+        state_action_values = self.agent_net(states).gather(1, actions.unsqueeze(-1)).squeeze(-1)
+
+        with torch.no_grad():
+            next_state_values = self.target_net(next_states).max(1)[0]
+            next_state_values[dones] = 0.0
+            next_state_values = next_state_values.detach()
+
+        expected_state_action_values = next_state_values * self.gamma + rewards
+
+        return F.mse_loss(state_action_values, expected_state_action_values)

audio_toolset/audio_augmentation.py

@@ -1,22 +1,38 @@
-import librosa
+try:
+    import librosa
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `librosa` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install librosa`.')
+
 import numpy as np
 
 
 class Speed(object):
 
-    def __init__(self, max_ratio=0.3, speed_factor=1):
-        self.speed_factor = speed_factor
-        self.max_ratio = max_ratio
+    def __init__(self, max_amount=0.3, speed_min=1, speed_max=1):
+        self.speed_max = speed_max if speed_max else 1
+        self.speed_min = speed_min if speed_min else 1
+        # noinspection PyTypeChecker
+        self.max_amount = min(max(0, max_amount), 1)
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
 
     def __call__(self, x):
-        if not all([self.speed_factor, self.max_ratio]):
+        if self.speed_min == 1 and self.speed_max == 1:
             return x
-        start = int(np.random.randint(0, x.shape[-1],1))
-        end = int((np.random.uniform(0, self.max_ratio, 1) * x.shape[-1]) + start)
+        start = int(np.random.randint(low=0, high=x.shape[-1], size=1))
+        width = np.random.uniform(low=0, high=self.max_amount, size=1) * x.shape[-1]
+        end = int(width + start)
         end = min(end, x.shape[-1])
         try:
-            speed_factor = float(np.random.uniform(min(self.speed_factor, 1), max(self.speed_factor, 1), 1))
-            aug_data = librosa.effects.time_stretch(x[start:end], speed_factor)
-            return np.concatenate((x[:start], aug_data, x[end:]), axis=0)[:x.shape[-1]]
+            speed_factor = float(np.random.uniform(low=self.speed_min, high=self.speed_max, size=1))
+            aug_data = librosa.effects.time_stretch(y=x[start:end], rate=speed_factor)
+            x_aug = np.concatenate((x[:start], aug_data, x[end:]), axis=0)[:x.shape[-1]]
+            if speed_factor > 1:
+                embedding = np.zeros_like(x)
+                embedding[:x_aug.shape[0]] = x_aug
+                x_aug = embedding
+            return x_aug
         except ValueError:
             return x

audio_toolset/audio_io.py

@@ -1,8 +1,16 @@
-import librosa
-from scipy.signal import butter, lfilter
-
 import numpy as np
 
+try:
+    import librosa
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `librosa` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install librosa`.')
+try:
+    from scipy.signal import butter, lfilter
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `scikit` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install scikit-learn`.')
+
 
 def scale_minmax(x, min_val=0.0, max_val=1.0):
     x_std = (x - x.min()) / (x.max() - x.min())
@@ -28,6 +36,9 @@ class MFCC(object):
     def __init__(self, **kwargs):
         self.__dict__.update(kwargs)
 
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
     def __call__(self, y):
         mfcc = librosa.feature.mfcc(y, **self.__dict__)
         return mfcc
@@ -35,27 +46,38 @@ class MFCC(object):
 
 class NormalizeLocal(object):
     def __init__(self):
-        self.cache: np.ndarray
         pass
 
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
     def __call__(self, x: np.ndarray):
+
+        x[np.isnan(x)] = 0
+        x[np.isinf(x)] = 0
+
         mean = x.mean()
         std = x.std() + 0.0001
 
         # Pytorch Version:
-        # x = x.__sub__(mean).__div__(std)
+        # tensor = tensor.__sub__(mean).__div__(std)
         # Numpy Version
         x = (x - mean) / std
+
         x[np.isnan(x)] = 0
         x[np.isinf(x)] = 0
+
         return x
 
 
 class NormalizeMelband(object):
     def __init__(self):
-        self.cache: np.ndarray
+
         pass
 
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
     def __call__(self, x: np.ndarray):
         mean = x.mean(-1).unsqueeze(-1)
         std = x.std(-1).unsqueeze(-1)
@@ -66,10 +88,13 @@ class NormalizeMelband(object):
         return x
 
 
-class AudioToMel(object):
-    def __init__(self, amplitude_to_db=False, power_to_db=False, **kwargs):
+class LibrosaAudioToMel(object):
+    def __init__(self, amplitude_to_db=False, power_to_db=False, **mel_kwargs):
         assert not all([amplitude_to_db, power_to_db]), "Choose amplitude_to_db or power_to_db, not both!"
-        self.mel_kwargs = kwargs
+        # Mel kwargs are:
+        #   sr   n_mels   n_fft   hop_length
+
+        self.mel_kwargs = mel_kwargs
         self.amplitude_to_db = amplitude_to_db
         self.power_to_db = power_to_db
 
@@ -89,6 +114,9 @@ class PowerToDB(object):
     def __init__(self, running_max=False):
         self.running_max = 0 if running_max else None
 
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
     def __call__(self, x):
         if self.running_max is not None:
             self.running_max = max(np.max(x), self.running_max)
@@ -100,6 +128,9 @@ class LowPass(object):
     def __init__(self, sr=16000):
         self.sr = sr
 
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
     def __call__(self, x):
         return butter_lowpass_filter(x, 1000, 1)
 
@@ -108,12 +139,16 @@ class MelToImage(object):
     def __init__(self):
         pass
 
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
     def __call__(self, x):
         # Source to Solution: https://stackoverflow.com/a/57204349
         mels = np.log(x + 1e-9)  # add small number to avoid log(0)
 
         # min-max scale to fit inside 8-bit range
-        img = scale_minmax(mels, 0, 255).astype(np.uint8)
-        img = np.flip(img, axis=0)  # put low frequencies at the bottom in image
+        img = scale_minmax(mels, 0, 255)
+        img = np.flip(img)  # put low frequencies at the bottom in image
         img = 255 - img  # invert. make black==more energy
+        img = img.astype(np.float)
         return img

audio_toolset/audio_to_mel_dataset.py

@@ -0,0 +1,71 @@
+import sys
+from pathlib import Path
+
+import pickle
+from abc import ABC
+from torch.utils.data import Dataset
+from torchvision.transforms import Compose
+
+from ml_lib.audio_toolset.audio_io import LibrosaAudioToMel, MelToImage
+from ml_lib.audio_toolset.mel_dataset import TorchMelDataset
+
+
+import librosa
+
+
+class LibrosaAudioToMelDataset(Dataset):
+
+    @property
+    def audio_file_duration(self):
+        return librosa.get_duration(sr=self.mel_kwargs.get('sr', None), filename=self.audio_path)
+
+    @property
+    def sampling_rate(self):
+        return self.mel_kwargs.get('sr', None)
+
+    def __init__(self, audio_file_path, label, sample_segment_len=0, sample_hop_len=0, reset=False,
+                 audio_augmentations=None, mel_augmentations=None, mel_kwargs=None, **kwargs):
+        super(LibrosaAudioToMelDataset, self).__init__()
+
+        # audio_file, sampling_rate = librosa.load(self.audio_path, sr=sampling_rate)
+        mel_kwargs.update(sr=mel_kwargs.get('sr', None) or librosa.get_samplerate(audio_file_path))
+        self.mel_kwargs = mel_kwargs
+        self.reset = reset
+        self.audio_path = Path(audio_file_path)
+
+        mel_folder_suffix = self.audio_path.parent.parent.name
+        self.mel_folder = Path(str(self.audio_path)
+                               .replace(mel_folder_suffix, f'{mel_folder_suffix}_mel_folder')).parent.parent
+
+        self.mel_file_path = self.mel_folder / f'{self.audio_path.stem}.npy'
+
+        self.audio_augmentations = audio_augmentations
+
+        self.dataset = TorchMelDataset(self.mel_file_path, sample_segment_len, sample_hop_len, label,
+                                       self.audio_file_duration, mel_kwargs['sr'], mel_kwargs['hop_length'],
+                                       mel_kwargs['n_mels'], transform=mel_augmentations)
+
+        self._mel_transform = Compose([LibrosaAudioToMel(power_to_db=False, **mel_kwargs),
+                                       MelToImage()
+                                       ])
+
+    def __getitem__(self, item):
+        return self.dataset[item]
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def build_mel(self):
+        if self.reset:
+            self.mel_file_path.unlink(missing_ok=True)
+        if not self.mel_file_path.exists():
+            self.mel_file_path.parent.mkdir(parents=True, exist_ok=True)
+            with self.audio_path.open(mode='rb') as audio_file:
+                raw_sample, _ = librosa.core.load(audio_file, sr=self.sampling_rate)
+            mel_sample = self._mel_transform(raw_sample)
+            with self.mel_file_path.open('wb') as mel_file:
+                pickle.dump(mel_sample, mel_file, protocol=pickle.HIGHEST_PROTOCOL)
+        else:
+            pass
+
+        return self.mel_file_path.exists()

audio_toolset/mel_augmentation.py

@@ -1,17 +1,20 @@
 import numpy as np
 
+from ml_lib.utils.transforms import _BaseTransformation
 
-class NoiseInjection(object):
 
-    def __init__(self, noise_factor: float, sigma=0.5, mu=0.5):
-        assert noise_factor >= 0, f'max_shift_ratio has to be greater then 0, but was: {noise_factor}.'
+class NoiseInjection(_BaseTransformation):
+
+    def __init__(self, noise_factor: float, sigma=1, mu=0):
+        super(NoiseInjection, self).__init__()
+        assert noise_factor >= 0, f'noise_factor has to be greater then 0, but was: {noise_factor}.'
         self.mu = mu
         self.sigma = sigma
         self.noise_factor = noise_factor
 
     def __call__(self, x: np.ndarray):
         if self.noise_factor:
-            noise = np.random.uniform(0, self.noise_factor, size=x.shape)
+            noise = np.random.normal(self.mu, self.sigma, size=x.shape) * self.noise_factor
             augmented_data = x + x * noise
             # Cast back to same data type
             augmented_data = augmented_data.astype(x.dtype)
@@ -20,14 +23,15 @@ class NoiseInjection(object):
             return x
 
 
-class LoudnessManipulator(object):
+class LoudnessManipulator(_BaseTransformation):
 
     def __init__(self, max_factor: float):
+        super(LoudnessManipulator, self).__init__()
         assert 1 > max_factor >= 0, f'max_shift_ratio has to be between [0,1], but was: {max_factor}.'
 
         self.max_factor = max_factor
 
-    def __call__(self, x: np.ndarray):
+    def __call__(self, x):
         if self.max_factor:
             augmented_data = x + x * (np.random.random() * self.max_factor)
             # Cast back to same data type
@@ -37,11 +41,12 @@ class LoudnessManipulator(object):
             return x
 
 
-class ShiftTime(object):
+class ShiftTime(_BaseTransformation):
 
     valid_shifts = ['right', 'left', 'any']
 
     def __init__(self, max_shift_ratio: float, shift_direction: str = 'any'):
+        super(ShiftTime, self).__init__()
         assert 1 > max_shift_ratio >= 0, f'max_shift_ratio has to be between [0,1], but was: {max_shift_ratio}.'
         assert shift_direction.lower() in self.valid_shifts, f'shift_direction has to be one of: {self.valid_shifts}'
         self.max_shift_ratio = max_shift_ratio
@@ -53,26 +58,27 @@ class ShiftTime(object):
             if self.shift_direction == 'right':
                 shift = -1 * shift
             elif self.shift_direction == 'any':
-                direction = np.random.choice([1, -1], 1)
+                direction = np.asscalar(np.random.choice([1, -1], 1))
                 shift = direction * shift
             augmented_data = np.roll(x, shift)
             # Set to silence for heading/ tailing
             shift = int(shift)
             if shift > 0:
-                augmented_data[:shift] = 0
+                augmented_data[:, :shift] = 0
             else:
-                augmented_data[shift:] = 0
+                augmented_data[:, shift:] = 0
             return augmented_data
         else:
             return x
 
 
-class MaskAug(object):
+class MaskAug(_BaseTransformation):
 
     w_idx = -1
     h_idx = -2
 
     def __init__(self, duration_ratio_max=0.3, mask_with_noise=True):
+        super(MaskAug, self).__init__()
         assertion = f'"duration_ratio" has to be within [0..1], but was: {duration_ratio_max}'
         if isinstance(duration_ratio_max, (tuple, list)):
             assert all([0 < max_val < 1 for max_val in duration_ratio_max]), assertion
@@ -87,9 +93,9 @@ class MaskAug(object):
     def __call__(self, x):
         for dim in (self.w_idx, self.h_idx):
             if self.duration_ratio_max[dim]:
-                start = int(np.random.choice(x.shape[dim], 1))
-                v_max = x.shape[dim] * self.duration_ratio_max[dim]
-                size = int(np.random.randint(0, v_max, 1))
+                start = np.asscalar(np.random.choice(x.shape[dim], 1))
+                v_max = int(x.shape[dim] * self.duration_ratio_max[dim])
+                size = np.asscalar(np.random.randint(0, v_max, 1))
                 end = int(min(start + size, x.shape[dim]))
                 size = end - start
                 if dim == self.w_idx:

audio_toolset/mel_dataset.py

@@ -0,0 +1,54 @@
+import time
+from pathlib import Path
+
+import pickle
+from torch.utils.data import Dataset
+
+from ml_lib.modules.util import AutoPadToShape
+
+
+class TorchMelDataset(Dataset):
+    def __init__(self, mel_path, sub_segment_len, sub_segment_hop_len, label, audio_file_len,
+                 sampling_rate, mel_hop_len, n_mels, transform=None, auto_pad_to_shape=True):
+        super(TorchMelDataset, self).__init__()
+        self.sampling_rate = int(sampling_rate)
+        self.audio_file_len = float(audio_file_len)
+        if auto_pad_to_shape and sub_segment_len:
+            self.padding = AutoPadToShape((int(n_mels), int(sub_segment_len)))
+        else:
+            self.padding = None
+        self.path = Path(mel_path)
+        self.sub_segment_len = int(sub_segment_len)
+        self.mel_hop_len = int(mel_hop_len)
+        self.sub_segment_hop_len = int(sub_segment_hop_len)
+        self.n = int((self.sampling_rate / self.mel_hop_len) * self.audio_file_len + 1)
+        if self.sub_segment_len and self.sub_segment_hop_len and (self.n - self.sub_segment_len) > 0:
+            self.offsets = list(range(0, self.n - self.sub_segment_len, self.sub_segment_hop_len))
+        else:
+            self.offsets = [0]
+        if len(self) == 0:
+            print('what happend here')
+        self.label = label
+        self.transform = transform
+
+    def __getitem__(self, item):
+        with self.path.open('rb') as mel_file:
+            mel_spec = pickle.load(mel_file, fix_imports=True)
+        start = self.offsets[item]
+        sub_segments_attributes_set = self.sub_segment_len and self.sub_segment_hop_len
+        sub_segment_length_smaller_then_tot_length = self.sub_segment_len < mel_spec.shape[1]
+
+        if sub_segments_attributes_set and sub_segment_length_smaller_then_tot_length:
+            duration = self.sub_segment_len
+        else:
+            duration = mel_spec.shape[1]
+
+        snippet = mel_spec[:, start: start + duration]
+        if self.transform:
+            snippet = self.transform(snippet)
+        if self.padding:
+            snippet = self.padding(snippet)
+        return self.path.__str__(), snippet, self.label
+
+    def __len__(self):
+        return len(self.offsets)

audio_toolset/mel_transforms.py

@@ -0,0 +1,27 @@
+from typing import Union
+
+import numpy as np
+
+
+class Normalize(object):
+
+    def __init__(self, min_db_level: Union[int, float]):
+        self.min_db_level = min_db_level
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
+    def __call__(self, s: np.ndarray) -> np.ndarray:
+        return np.clip((s - self.min_db_level) / -self.min_db_level, 0, 1)
+
+
+class DeNormalize(object):
+
+    def __init__(self, min_db_level: Union[int, float]):
+        self.min_db_level = min_db_level
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
+    def __call__(self, s: np.ndarray) -> np.ndarray:
+        return (np.clip(s, 0, 1) * -self.min_db_level) + self.min_db_level

evaluation/classification.py

@@ -1,13 +1,21 @@
-import matplotlib.pyplot as plt
-from sklearn.metrics import roc_curve, auc
+try:
+    import matplotlib.pyplot as plt
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `matplotlib` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install matplotlib`.')
+try:
+    from sklearn.metrics import roc_curve, auc, recall_score
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `sklearn` plugins which are not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install scikit-learn`.')
 
 
 class ROCEvaluation(object):
 
     linewidth = 2
 
-    def __init__(self, plot_roc=False):
-        self.plot_roc = plot_roc
+    def __init__(self, plot=False):
+        self.plot = plot
         self.epoch = 0
 
     def __call__(self, prediction, label):
@@ -15,7 +23,7 @@ class ROCEvaluation(object):
         # Compute ROC curve and ROC area
         fpr, tpr, _ = roc_curve(prediction, label)
         roc_auc = auc(fpr, tpr)
-        if self.plot_roc:
+        if self.plot:
             _ = plt.gcf()
             plt.plot(fpr, tpr, color='darkorange', lw=self.linewidth, label=f'ROC curve (area = {roc_auc})')
             self._prepare_fig()
@@ -32,3 +40,32 @@ class ROCEvaluation(object):
         fig.legend(loc="lower right")
 
         return fig
+
+
+class UAREvaluation(object):
+
+    def __init__(self, labels: list, plot=False):
+        self.labels = labels
+        self.plot_roc = plot
+        self.epoch = 0
+
+    def __call__(self, prediction, label):
+
+        # Compute uar score - UnweightedAverageRecal
+
+        uar_score = recall_score(label, prediction, labels=self.labels, average='macro',
+                                 sample_weight=None, zero_division='warn')
+        return uar_score
+
+    def _prepare_fig(self):
+        raise NotImplementedError # TODO Implement a nice visualization
+        fig = plt.gcf()
+        ax = plt.gca()
+        plt.plot([0, 1], [0, 1], color='navy', lw=self.linewidth, linestyle='--')
+        plt.xlim([0.0, 1.0])
+        plt.ylim([0.0, 1.05])
+        plt.xlabel('False Positive Rate')
+        plt.ylabel('True Positive Rate')
+        fig.legend(loc="lower right")
+
+        return fig

metrics/_base_score.py

@@ -0,0 +1,13 @@
+from abc import ABC
+
+
+class _BaseScores(ABC):
+
+    def __init__(self, lightning_model):
+        self.model = lightning_model
+        pass
+
+    def __call__(self, outputs):
+        # summary_dict = dict()
+        # return summary_dict
+        raise NotImplementedError

metrics/attention_rollout.py

@@ -0,0 +1,47 @@
+import numpy as np
+
+from einops import reduce
+
+
+import torch
+from sklearn.ensemble import IsolationForest
+from sklearn.metrics import recall_score, roc_auc_score, average_precision_score
+
+from ml_lib.metrics._base_score import _BaseScores
+
+
+class AttentionRollout(_BaseScores):
+
+    def __init__(self, *args):
+        super(AttentionRollout, self).__init__(*args)
+        pass
+
+    def __call__(self, outputs):
+        summary_dict = dict()
+        #######################################################################################
+        # Additional Score  -  Histogram Distances - Image Plotting
+        #######################################################################################
+        #
+        # INIT
+        attn_weights = [output['attn_weights'].cpu().numpy() for output in outputs]
+        attn_reduce_heads = [reduce(x, '') for x in attn_weights]
+
+        if self.model.params.use_residual:
+            residual_att = np.eye(att_mat.shape[1])[None, ...]
+            aug_att_mat = att_mat + residual_att
+            aug_att_mat = aug_att_mat / aug_att_mat.sum(axis=-1)[..., None]
+        else:
+            aug_att_mat = att_mat
+
+        joint_attentions = np.zeros(aug_att_mat.shape)
+
+        layers = joint_attentions.shape[0]
+        joint_attentions[0] = aug_att_mat[0]
+        for i in np.arange(1, layers):
+            joint_attentions[i] = aug_att_mat[i].dot(joint_attentions[i - 1])
+
+
+
+
+
+

metrics/binary_class_classifictaion.py

@@ -0,0 +1,68 @@
+import numpy as np
+
+import torch
+from sklearn.ensemble import IsolationForest
+from sklearn.metrics import recall_score, roc_auc_score, average_precision_score
+
+from ml_lib.metrics._base_score import _BaseScores
+from ml_lib.utils.tools import to_one_hot
+
+
+class BinaryScores(_BaseScores):
+
+    def __init__(self, *args):
+        super(BinaryScores, self).__init__(*args)
+
+    def __call__(self, outputs):
+        summary_dict = dict()
+
+        # Additional Score like the unweighted Average Recall:
+        #########################
+        # INIT
+        if isinstance(outputs['batch_y'], torch.Tensor):
+            y_true = outputs['batch_y'].cpu().numpy()
+        else:
+            y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
+
+        if isinstance(outputs['y'], torch.Tensor):
+            y_pred = outputs['y'].cpu().numpy()
+        else:
+            y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().float().numpy()
+
+        # UnweightedAverageRecall
+        # y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
+        # y_pred = torch.cat([output['element_wise_recon_error'] for output in outputs]).squeeze().cpu().numpy()
+
+        # How to apply a threshold manualy
+        # y_pred = (y_pred >= 0.5).astype(np.float32)
+
+        # How to apply a threshold by IF (Isolation Forest)
+        clf = IsolationForest()
+        y_score = clf.fit_predict(y_pred.reshape(-1, 1))
+        y_score = (np.asarray(y_score) == -1).astype(np.float32)
+
+        uar_score = recall_score(y_true, y_score, labels=[0, 1], average='macro',
+                                 sample_weight=None, zero_division='warn')
+        summary_dict.update(dict(uar_score=uar_score))
+        #########################
+        # Precission
+        precision_score = average_precision_score(y_true, y_score)
+        summary_dict.update(dict(precision_score=precision_score))
+
+        #########################
+        # AUC
+        try:
+            auc_score = roc_auc_score(y_true=y_true, y_score=y_score)
+            summary_dict.update(dict(auc_score=auc_score))
+        except ValueError:
+            summary_dict.update(dict(auc_score=-1))
+
+        #########################
+        # pAUC
+        try:
+            pauc = roc_auc_score(y_true=y_true, y_score=y_score, max_fpr=0.15)
+            summary_dict.update(dict(pauc_score=pauc))
+        except ValueError:
+            summary_dict.update(dict(pauc_score=-1))
+
+        return summary_dict

metrics/generative_task_evaluation.py

@@ -0,0 +1,68 @@
+from itertools import cycle
+
+import numpy as np
+import torch
+from sklearn.metrics import roc_curve, auc, roc_auc_score, ConfusionMatrixDisplay, confusion_matrix
+from scipy.spatial.distance import cdist
+
+from ml_lib.metrics._base_score import _BaseScores
+
+from matplotlib import pyplot as plt
+
+
+class GenerativeTaskEval(_BaseScores):
+
+    def __init__(self, *args):
+        super(GenerativeTaskEval, self).__init__(*args)
+        pass
+
+    def __call__(self, outputs):
+        summary_dict = dict()
+        #######################################################################################
+        # Additional Score  -  Histogram Distances - Image Plotting
+        #######################################################################################
+        #
+        # INIT
+        y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
+
+        y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().numpy()
+
+        attn_weights = torch.cat([output['attn_weights'] for output in outputs]).squeeze().cpu().numpy()
+
+        ######################################################################################
+        #
+        # Histogram comparission
+
+        y_true_hist = np.histogram(y_true, bins=128)[0]  # Todo: Find a better value
+        y_pred_hist = np.histogram(y_pred, bins=128)[0]  # Todo: Find a better value
+
+        # L2 norm == euclidean distance
+        hist_euc_dist = cdist(np.expand_dims(y_true_hist, axis=0), np.expand_dims(y_pred_hist, axis=0),
+                              metric='euclidean')
+
+        # Manhattan Distance
+        hist_manhattan_dist = cdist(np.expand_dims(y_true_hist, axis=0), np.expand_dims(y_pred_hist, axis=0),
+                                    metric='cityblock')
+
+        summary_dict.update(hist_manhattan_dist=hist_manhattan_dist, hist_euc_dist=hist_euc_dist)
+
+        #######################################################################################
+        #
+        idx = np.random.choice(np.arange(y_true.shape[0]), 1).item()
+
+        ax = plt.imshow(y_true[idx].squeeze())
+        # Plot using a small number of colors, with unevenly spaced boundaries.
+        ax2 = plt.imshow(attn_weights[idx].sq, interpolation='nearest', aspect='auto', extent=ax.get_extent())
+        self.model.logger.log_image('ROC', image=plt.gcf(), step=self.model.current_epoch)
+        plt.clf()
+
+
+        #######################################################################################
+        #
+
+
+        #######################################################################################
+        #
+
+        plt.close('all')
+        return summary_dict

metrics/multi_class_classification.py

@@ -0,0 +1,142 @@
+from itertools import cycle
+
+import numpy as np
+import torch
+from sklearn.metrics import f1_score, roc_curve, auc, roc_auc_score, ConfusionMatrixDisplay, confusion_matrix, \
+    recall_score
+
+from ml_lib.metrics._base_score import _BaseScores
+from ml_lib.utils.tools import to_one_hot
+
+from matplotlib import pyplot as plt
+
+
+class MultiClassScores(_BaseScores):
+
+    def __init__(self, *args):
+        super(MultiClassScores, self).__init__(*args)
+        pass
+
+    def __call__(self, outputs, class_names=None):
+        summary_dict = dict()
+        class_names = class_names or range(self.model.params.n_classes)
+        #######################################################################################
+        # Additional Score  -  UAR  -  ROC  -  Conf. Matrix  -  F1
+        #######################################################################################
+        #
+        # INIT
+        if isinstance(outputs['batch_y'], torch.Tensor):
+            y_true = outputs['batch_y'].cpu().numpy()
+        else:
+            y_true = torch.cat([output['batch_y'] for output in outputs]).cpu().numpy()
+        y_true_one_hot = to_one_hot(y_true, self.model.params.n_classes)
+
+        if isinstance(outputs['y'], torch.Tensor):
+            y_pred = outputs['y'].cpu().numpy()
+        else:
+            y_pred = torch.cat([output['y'] for output in outputs]).squeeze().cpu().float().numpy()
+        y_pred_max = np.argmax(y_pred, axis=1)
+
+        class_names = {val: key for val, key in enumerate(class_names)}
+        ######################################################################################
+        #
+        # F1 SCORE
+        micro_f1_score = f1_score(y_true, y_pred_max, labels=None, pos_label=1, average='micro', sample_weight=None,
+                                  zero_division=True)
+        macro_f1_score = f1_score(y_true, y_pred_max, labels=None, pos_label=1, average='macro', sample_weight=None,
+                                  zero_division=True)
+        summary_dict.update(dict(micro_f1_score=micro_f1_score, macro_f1_score=macro_f1_score))
+        ######################################################################################
+        #
+        # Unweichted Average Recall
+        uar = recall_score(y_true, y_pred_max, labels=[0, 1, 2, 3, 4], average='macro',
+                           sample_weight=None, zero_division='warn')
+        summary_dict.update(dict(uar_score=uar))
+        #######################################################################################
+        #
+        # ROC Curve
+
+        # Compute ROC curve and ROC area for each class
+        fpr = dict()
+        tpr = dict()
+        roc_auc = dict()
+        for i in range(self.model.params.n_classes):
+            fpr[i], tpr[i], _ = roc_curve(y_true_one_hot[:, i], y_pred[:, i])
+            roc_auc[i] = auc(fpr[i], tpr[i])
+
+        # Compute micro-average ROC curve and ROC area
+        fpr["micro"], tpr["micro"], _ = roc_curve(y_true_one_hot.ravel(), y_pred.ravel())
+        roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
+
+        # First aggregate all false positive rates
+        all_fpr = np.unique(np.concatenate([fpr[i] for i in range(self.model.params.n_classes)]))
+
+        # Then interpolate all ROC curves at this points
+        mean_tpr = np.zeros_like(all_fpr)
+        for i in range(self.model.params.n_classes):
+            mean_tpr += np.interp(all_fpr, fpr[i], tpr[i])
+
+        # Finally average it and compute AUC
+        mean_tpr /= self.model.params.n_classes
+
+        fpr["macro"] = all_fpr
+        tpr["macro"] = mean_tpr
+        roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
+
+        # Plot all ROC curves
+        plt.figure()
+        plt.plot(fpr["micro"], tpr["micro"],
+                 label=f'micro ROC ({round(roc_auc["micro"], 2)})',
+                 color='deeppink', linestyle=':', linewidth=4)
+
+        plt.plot(fpr["macro"], tpr["macro"],
+                 label=f'macro ROC({round(roc_auc["macro"], 2)})',
+                 color='navy', linestyle=':', linewidth=4)
+
+        colors = cycle(['firebrick', 'orangered', 'gold', 'olive', 'limegreen', 'aqua',
+                        'dodgerblue', 'slategrey', 'royalblue', 'indigo', 'fuchsia'], )
+
+        for i, color in zip(range(self.model.params.n_classes), colors):
+            plt.plot(fpr[i], tpr[i], color=color, lw=2, label=f'{class_names[i]} ({round(roc_auc[i], 2)})')
+
+        plt.plot([0, 1], [0, 1], 'k--', lw=2)
+        plt.xlim([0.0, 1.0])
+        plt.ylim([0.0, 1.05])
+        plt.xlabel('False Positive Rate')
+        plt.ylabel('True Positive Rate')
+        plt.legend(loc="lower right")
+
+        self.model.logger.log_image('ROC', image=plt.gcf(), step=self.model.current_epoch)
+        # self.model.logger.log_image('ROC', image=plt.gcf(), step=self.model.current_epoch, ext='pdf')
+        plt.clf()
+
+        #######################################################################################
+        #
+        # ROC AUC SCORE
+
+        try:
+            macro_roc_auc_ovr = roc_auc_score(y_true_one_hot, y_pred, multi_class="ovr",
+                                              average="macro")
+            summary_dict.update(macro_roc_auc_ovr=macro_roc_auc_ovr)
+        except ValueError:
+            micro_roc_auc_ovr = roc_auc_score(y_true_one_hot, y_pred, multi_class="ovr",
+                                              average="micro")
+            summary_dict.update(micro_roc_auc_ovr=micro_roc_auc_ovr)
+
+        #######################################################################################
+        #
+        # Confusion matrix
+        fig1, ax1 = plt.subplots(dpi=96)
+        cm = confusion_matrix([class_names[x] for x in y_true], [class_names[x] for x in y_pred_max],
+                              labels=[class_names[key] for key in class_names.keys()],
+                              normalize='true')
+        disp = ConfusionMatrixDisplay(confusion_matrix=cm,
+                                      display_labels=[class_names[i] for i in range(self.model.params.n_classes)]
+                                      )
+        disp.plot(include_values=True, ax=ax1)
+
+        self.model.logger.log_image('Confusion_Matrix', image=fig1, step=self.model.current_epoch)
+        # self.model.logger.log_image('Confusion_Matrix', image=disp.figure_, step=self.model.current_epoch, ext='pdf')
+
+        plt.close('all')
+        return summary_dict

modules/blocks.py

@@ -1,11 +1,17 @@
+import warnings
+
+from pathlib import Path
 from typing import Union
 
 import torch
-import warnings
 
 from torch import nn
+from torch.nn import functional as F
 
-from modules.utils import AutoPad, Interpolate, ShapeMixin, F_x, Flatten
+import sys
+sys.path.append(str(Path(__file__).parent))
+
+from .util import AutoPad, Interpolate, ShapeMixin, F_x, Flatten
 
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
@@ -15,23 +21,24 @@ DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 ###################
 class LinearModule(ShapeMixin, nn.Module):
 
-    def __init__(self, in_shape, out_features, bias=True, activation=None,
-                 norm=False, dropout: Union[int, float] = 0, **kwargs):
-        warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
+    def __init__(self, in_shape, out_features, use_bias=True, activation=None,
+                 use_norm=False, dropout: Union[int, float] = 0, **kwargs):
+        if list(kwargs.keys()):
+            warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
         super(LinearModule, self).__init__()
 
         self.in_shape = in_shape
         self.flat = Flatten(self.in_shape) if isinstance(self.in_shape, (tuple, list)) else F_x(in_shape)
         self.dropout = nn.Dropout(dropout) if dropout else F_x(self.flat.shape)
-        self.norm = nn.BatchNorm1d(self.flat.shape) if norm else F_x(self.flat.shape)
-        self.linear = nn.Linear(self.flat.shape, out_features, bias=bias)
+        self.norm = nn.LayerNorm(self.flat.shape) if use_norm else F_x(self.flat.shape)
+        self.linear = nn.Linear(self.flat.shape, out_features, bias=use_bias)
         self.activation = activation() if activation else F_x(self.linear.out_features)
 
     def forward(self, x):
         tensor = self.flat(x)
         tensor = self.dropout(tensor)
         tensor = self.norm(tensor)
-        tensor = self.linear(tensor)
+        tensor = self.linear(tensor.float())
         tensor = self.activation(tensor)
         return tensor
 
@@ -39,14 +46,22 @@ class LinearModule(ShapeMixin, nn.Module):
 class ConvModule(ShapeMixin, nn.Module):
 
     def __init__(self, in_shape, conv_filters, conv_kernel, activation: nn.Module = nn.ELU, pooling_size=None,
-                 bias=True, norm=False, dropout: Union[int, float] = 0,
+                 bias=True, use_norm=False, dropout: Union[int, float] = 0, trainable: bool = True,
                  conv_class=nn.Conv2d, conv_stride=1, conv_padding=0, **kwargs):
         super(ConvModule, self).__init__()
         assert isinstance(in_shape, (tuple, list)), f'"in_shape" should be a [list, tuple], but was {type(in_shape)}'
         assert len(in_shape) == 3, f'Length should be 3, but was {len(in_shape)}'
-        warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
+        if len(kwargs.keys()):
+            warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
+        if use_norm and not trainable:
+            warnings.warn('You set this module to be not trainable but the running norm is active.\n' +
+                          'We set it to "eval" mode.\n' +
+                          'Keep this in mind if you do a finetunning or retraining step.'
+                          )
+
         # Module Parameters
         self.in_shape = in_shape
+        self.trainable = trainable
         in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
 
         # Convolution Parameters
@@ -56,13 +71,19 @@ class ConvModule(ShapeMixin, nn.Module):
         self.conv_kernel = conv_kernel
 
         # Modules
-        self.activation = activation() or F_x(None)
+        self.activation = activation() or nn.Identity()
+        self.norm = nn.LayerNorm(self.in_shape, eps=1e-04) if use_norm else F_x(None)
         self.dropout = nn.Dropout2d(dropout) if dropout else F_x(None)
         self.pooling = nn.MaxPool2d(pooling_size) if pooling_size else F_x(None)
-        self.norm = nn.BatchNorm2d(in_channels, eps=1e-04) if norm else F_x(None)
         self.conv = conv_class(in_channels, self.conv_filters, self.conv_kernel, bias=bias,
                                padding=self.padding, stride=self.stride
                                )
+        if not self.trainable:
+            for param in self.parameters():
+                param.requires_grad = False
+            self.norm = self.norm.eval()
+        else:
+            pass
 
     def forward(self, x):
         tensor = self.norm(x)
@@ -73,13 +94,49 @@ class ConvModule(ShapeMixin, nn.Module):
         return tensor
 
 
+class PreInitializedConvModule(ShapeMixin, nn.Module):
+
+    def __init__(self, in_shape, weight_matrix):
+        super(PreInitializedConvModule, self).__init__()
+        self.in_shape = in_shape
+        self.weight_matrix = weight_matrix
+        raise NotImplementedError
+        # ToDo Get the weight_matrix shape and init a conv_module of similar size,
+        #      override the weights then.
+
+    def forward(self, x):
+        x = torch.matmul(x, self.weight_matrix)  # ToDo: This is an Placeholder
+        return x
+
+
+class SobelFilter(ShapeMixin, nn.Module):
+
+    def __init__(self, in_shape):
+        super(SobelFilter, self).__init__()
+        self.in_shape = in_shape
+
+        self.sobel_x = torch.tensor([[1, 0, -1], [2, 0, -2], [1, 0, -1]]).view(1, 1, 3, 3)
+        self.sobel_y = torch.tensor([[1, 2, 1], [0, 0, 0], [-1, 2, -1]]).view(1, 1, 3, 3)
+
+    def forward(self, x):
+        # Apply Filters
+        g_x = F.conv2d(x, self.sobel_x)
+        g_y = F.conv2d(x, self.sobel_y)
+        # Calculate the Edge
+        g = torch.add(*[torch.pow(tensor, 2) for tensor in [g_x, g_y]])
+        # Calculate the Gradient
+        g_grad = torch.atan2(g_x, g_y)
+        return g_x, g_y, g, g_grad
+
+
 class DeConvModule(ShapeMixin, nn.Module):
 
     def __init__(self, in_shape, conv_filters, conv_kernel, conv_stride=1, conv_padding=0,
                  dropout: Union[int, float] = 0, autopad=0,
                  activation: Union[None, nn.Module] = nn.ReLU, interpolation_scale=0,
-                 bias=True, norm=False):
+                 bias=True, use_norm=False, **kwargs):
         super(DeConvModule, self).__init__()
+        warnings.warn(f'The following arguments have been ignored: \n {list(kwargs.keys())}')
         in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
         self.padding = conv_padding
         self.conv_kernel = conv_kernel
@@ -89,8 +146,8 @@ class DeConvModule(ShapeMixin, nn.Module):
 
         self.autopad = AutoPad() if autopad else lambda x: x
         self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else lambda x: x
-        self.norm = nn.BatchNorm2d(in_channels, eps=1e-04) if norm else lambda x: x
-        self.dropout = nn.Dropout2d(dropout) if dropout else lambda x: x
+        self.norm = nn.LayerNorm(in_channels, eps=1e-04) if use_norm else F_x(self.in_shape)
+        self.dropout = nn.Dropout2d(dropout) if dropout else F_x(self.in_shape)
         self.de_conv = nn.ConvTranspose2d(in_channels, self.conv_filters, self.conv_kernel, bias=bias,
                                           padding=self.padding, stride=self.stride)
 
@@ -109,14 +166,13 @@ class DeConvModule(ShapeMixin, nn.Module):
 
 class ResidualModule(ShapeMixin, nn.Module):
 
-    def __init__(self, in_shape, module_class, n, norm=False, **module_parameters):
+    def __init__(self, in_shape, module_class, n, use_norm=False, **module_parameters):
         assert n >= 1
         super(ResidualModule, self).__init__()
         self.in_shape = in_shape
         module_parameters.update(in_shape=in_shape)
-        if norm:
-            self.norm = nn.BatchNorm1d if len(self.in_shape) <= 2 else nn.BatchNorm2d
-            self.norm = self.norm(self.in_shape if isinstance(self.in_shape, int) else self.in_shape[0])
+        if use_norm:
+            self.norm = nn.LayerNorm(self.in_shape if isinstance(self.in_shape, int) else self.in_shape[0])
         else:
             self.norm = F_x(self.in_shape)
         self.activation = module_parameters.get('activation', None)
@@ -128,8 +184,9 @@ class ResidualModule(ShapeMixin, nn.Module):
         assert self.in_shape == self.shape, f'The in_shape: {self.in_shape} - must match the out_shape: {self.shape}.'
 
     def forward(self, x):
+        tensor = self.norm(x)
         for module in self.residual_block:
-            tensor = module(x)
+            tensor = module(tensor)
 
         # noinspection PyUnboundLocalVariable
         tensor = tensor + x
@@ -155,3 +212,106 @@ class RecurrentModule(ShapeMixin, nn.Module):
     def forward(self, x):
         tensor = self.rnn(x)
         return tensor
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout=0., activation=nn.GELU):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, hidden_dim),
+            activation() or F_x(None),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, dim),
+            activation() or F_x(None),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, heads=8, head_dim=64, dropout=0.):
+        super().__init__()
+        inner_dim = head_dim * heads
+        project_out = not (heads == 1 and head_dim == dim)
+
+        self.heads = heads
+        self.scale = head_dim ** -0.5
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        ) if project_out else nn.Identity()
+
+    def forward(self, x, mask=None, return_attn_weights=False):
+        from einops import rearrange, repeat
+        # noinspection PyTupleAssignmentBalance
+        b, n, _, h = *x.shape, self.heads
+
+        qkv = self.to_qkv(x).chunk(3, dim=-1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), qkv)
+
+        dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
+
+        if mask is not None:
+            mask_value = -torch.finfo(dots.dtype).max
+            mask = F.pad(mask.flatten(1), (1, 0), value=True)
+            assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'
+            mask = mask[:, None, :] * mask[:, :, None]
+            mask = repeat(mask, 'b n d -> b h n d', h=h)            # My addition
+            dots.masked_fill_(~mask, mask_value)
+            # dots.masked_fill_(mask, mask_value)                     # My addition
+            del mask
+
+        attn = dots.softmax(dim=-1)
+
+        out = torch.einsum('bhij,bhjd->bhid', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out = self.to_out(out)
+        if return_attn_weights:
+            return out, attn
+        else:
+            return out
+
+
+class TransformerModule(ShapeMixin, nn.Module):
+
+    def __init__(self, in_shape, depth, heads, mlp_dim, head_dim=32, dropout=None, use_norm=False,
+                 activation=nn.GELU, use_residual=True):
+        super(TransformerModule, self).__init__()
+
+        self.in_shape = in_shape
+        self.use_residual = use_residual
+
+        self.flat = Flatten(self.in_shape) if isinstance(self.in_shape, (tuple, list)) else F_x(in_shape)
+
+        self.embedding_dim = self.flat.flat_shape
+        self.norm = nn.LayerNorm(self.embedding_dim) if use_norm else F_x(None)
+        self.attns = nn.ModuleList([Attention(self.embedding_dim, heads=heads, dropout=dropout, head_dim=head_dim)
+                                    for _ in range(depth)])
+        self.mlps = nn.ModuleList([FeedForward(self.embedding_dim, mlp_dim, dropout=dropout, activation=activation)
+                                   for _ in range(depth)])
+
+    def forward(self, x, mask=None, return_attn_weights=False, **_):
+        tensor = self.flat(x)
+        attn_weights = list()
+
+        for attn, mlp in zip(self.attns, self.mlps):
+            # Attention
+            attn_tensor = self.norm(tensor)
+            if return_attn_weights:
+                attn_tensor, attn_weight = attn(attn_tensor, mask=mask, return_attn_weights=return_attn_weights)
+                attn_weights.append(attn_weight)
+            else:
+                attn_tensor = attn(attn_tensor, mask=mask)
+            tensor = tensor + attn_tensor if self.use_residual else attn_tensor
+
+            # MLP
+            mlp_tensor = self.norm(tensor)
+            mlp_tensor = mlp(mlp_tensor)
+            tensor = tensor + mlp_tensor if self.use_residual else mlp_tensor
+
+        return (tensor, attn_weights) if return_attn_weights else tensor

modules/geometric_blocks.py

@@ -0,0 +1,65 @@
+import torch
+from torch import nn
+from torch.nn import ReLU
+try:
+    from torch_geometric.nn import PointConv, fps, radius, global_max_pool, knn_interpolate
+except ImportError:
+    print('Install torch-geometric to use this package.')
+
+
+class SAModule(torch.nn.Module):
+
+    def __init__(self, ratio, r, nn):
+        super(SAModule, self).__init__()
+        self.ratio = ratio
+        self.r = r
+        self.conv = PointConv(nn)
+
+    def forward(self, x, pos, batch):
+        idx = fps(pos, batch, ratio=self.ratio)
+        row, col = radius(pos, pos[idx], self.r, batch, batch[idx],
+                          max_num_neighbors=64)
+        edge_index = torch.stack([col, row], dim=0)
+        x = self.conv(x, (pos, pos[idx]), edge_index)
+        pos, batch = pos[idx], batch[idx]
+        return x, pos, batch
+
+
+class GlobalSAModule(nn.Module):
+    def __init__(self, nn, channels=3):
+        super(GlobalSAModule, self).__init__()
+        self.nn = nn
+        self.channels = channels
+
+    def forward(self, x, pos, batch):
+        x = self.nn(torch.cat([x, pos], dim=1))
+        x = global_max_pool(x, batch)
+        pos = pos.new_zeros((x.size(0), self.channels))
+        batch = torch.arange(x.size(0), device=batch.device)
+        return x, pos, batch
+
+
+class MLP(nn.Module):
+    def __init__(self, channels, norm=True):
+        super(MLP, self).__init__()
+        self.net = nn.Sequential(*[
+            nn.Sequential(nn.Linear(channels[i - 1], channels[i]), ReLU(), nn.BatchNorm1d(channels[i]))
+            for i in range(1, len(channels))
+        ]).double()
+
+    def forward(self, x, *args, **kwargs):
+        return self.net(x)
+
+
+class FPModule(torch.nn.Module):
+    def __init__(self, k, nn):
+        super(FPModule, self).__init__()
+        self.k = k
+        self.nn = nn
+
+    def forward(self, x, pos, batch, x_skip, pos_skip, batch_skip):
+        x = knn_interpolate(x, pos, pos_skip, batch, batch_skip, k=self.k)
+        if x_skip is not None:
+            x = torch.cat([x, x_skip], dim=1)
+        x = self.nn(x)
+        return x, pos_skip, batch_skip

modules/model_parts.py

@@ -1,80 +1,144 @@
 #
 # Full Model Parts
 ###################
+from argparse import Namespace
+from functools import reduce
+from typing import Union, List, Tuple
+
 import torch
+from abc import ABC
+from operator import mul
 from torch import nn
+from torch.utils.data import DataLoader
 
-from modules.utils import ShapeMixin
+from .blocks import ConvModule, DeConvModule, LinearModule
+
+from .util import ShapeMixin, LightningBaseModule, Flatten
 
 
-class Generator(nn.Module):
-    @property
-    def shape(self):
-        x = torch.randn(self.lat_dim).unsqueeze(0)
-        output = self(x)
-        return output.shape[1:]
+class AEBaseModule(LightningBaseModule, ABC):
 
-    # noinspection PyUnresolvedReferences
-    def __init__(self, out_channels, re_shape, lat_dim, use_norm=False, use_bias=True, dropout: Union[int, float] = 0,
-                 filters: List[int] = None, activation=nn.ReLU):
+    def generate_random_image(self, dataloader: Union[None, str, DataLoader] = None,
+                              lat_min: Union[Tuple, List, None] = None,
+                              lat_max: Union[Tuple, List, None] = None):
+
+        assert bool(dataloader) ^ bool(lat_min and lat_max), 'Decide wether to give min, max or a dataloader, not both.'
+
+        min_max = self._find_min_max(dataloader) if dataloader else [None, None]
+        # assert not any([tensor is None for tensor in min_max])
+        lat_min = torch.as_tensor(lat_min or min_max[0])
+        lat_max = lat_max or min_max[1]
+
+        random_z = torch.rand((1, self.lat_dim))
+        random_z = random_z * (abs(lat_min) + lat_max) - abs(lat_min)
+
+        return self.decoder(random_z).squeeze()
+
+    def encode(self, x):
+        if len(x.shape) == 3:
+            x = x.unsqueeze(0)
+        return self.encoder(x)
+
+    def _find_min_max(self, dataloader):
+        encodings = list()
+        for batch in dataloader:
+            encodings.append(self.encode(batch))
+        encodings = torch.cat(encodings, dim=0)
+        min_lat = encodings.min(dim=1)
+        max_lat = encodings.max(dim=1)
+        return min_lat, max_lat
+
+    def decode_lat_evenly(self, n: int,
+                          dataloader: Union[None, str, DataLoader] = None,
+                          lat_min: Union[Tuple, List, None] = None,
+                          lat_max: Union[Tuple, List, None] = None):
+        assert bool(dataloader) ^ bool(lat_min and lat_max), 'Decide wether to give min, max or a dataloader, not both.'
+
+        min_max = self._find_min_max(dataloader) if dataloader else [None, None]
+
+        lat_min = lat_min or min_max[0]
+        lat_max = lat_max or min_max[1]
+
+        random_latent_samples = torch.stack([torch.linspace(lat_min[i].item(), lat_max[i].item(), n)
+                                             for i in range(self.params.lat_dim)], dim=-1).cpu().detach()
+        return self.decode(random_latent_samples).cpu().detach()
+
+    def decode(self, z):
+        try:
+            if len(z.shape) == 1:
+                z = z.unsqueeze(0)
+        except AttributeError:
+            # Does not seem to be a tensor.
+            pass
+        return self.decoder(z).squeeze()
+
+    def encode_and_restore(self, x):
+        x = self.transfer_batch_to_device(x, self.device)
+        if len(x.shape) == 3:
+            x = x.unsqueeze(0)
+        z = self.encode(x)
+        try:
+            z = z.squeeze()
+        except AttributeError:
+            # Does not seem to be a tensor.
+            pass
+        x_hat = self.decode(z)
+
+        return Namespace(main_out=x_hat.squeeze(), latent_out=z)
+
+
+class Generator(ShapeMixin, nn.Module):
+
+    def __init__(self, in_shape, out_channels, re_shape, use_norm=False, use_bias=True,
+                 dropout: Union[int, float] = 0, interpolations: List[int] = None,
+                 filters: List[int] = None, kernels: List[int] = None, activation=nn.ReLU,
+                 **kwargs):
         super(Generator, self).__init__()
-        assert filters, '"Filters" has to be a list of int len 3'
-        self.filters = filters
-        self.activation = activation
-        self.inner_activation = activation()
+        assert filters, '"Filters" has to be a list of int.'
+        assert filters, '"Filters" has to be a list of int.'
+        kernels = kernels if kernels else [3] * len(filters)
+        assert len(filters) == len(kernels), '"Filters" and "Kernels" has to be of same length.'
+
+        interpolations = interpolations or [2, 2, 2]
+
+        self.in_shape = in_shape
+        self.activation = activation()
         self.out_activation = None
-        self.lat_dim = lat_dim
         self.dropout = dropout
-        self.l1 = nn.Linear(self.lat_dim, reduce(mul, re_shape), bias=use_bias)
+        self.l1 = LinearModule(in_shape, reduce(mul, re_shape), bias=use_bias, activation=activation)
         # re_shape = (self.feature_mixed_dim // reduce(mul, re_shape[1:]), ) + tuple(re_shape[1:])
 
-        self.flat = Flatten(to=re_shape)
+        self.flat = Flatten(self.l1.shape, to=re_shape)
+        self.de_conv_list = nn.ModuleList()
 
-        self.deconv1 = DeConvModule(re_shape, conv_filters=self.filters[0],
-                                    conv_kernel=5,
-                                    conv_padding=2,
-                                    conv_stride=1,
-                                    normalize=use_norm,
-                                    activation=self.activation,
-                                    interpolation_scale=2,
-                                    dropout=self.dropout
-                                    )
+        last_shape = re_shape
+        for conv_filter, conv_kernel, interpolation in zip(reversed(filters), kernels, interpolations):
+            # noinspection PyTypeChecker
+            self.de_conv_list.append(DeConvModule(last_shape, conv_filters=conv_filter,
+                                                  conv_kernel=conv_kernel,
+                                                  conv_padding=conv_kernel-2,
+                                                  conv_stride=1,
+                                                  normalize=use_norm,
+                                                  activation=activation,
+                                                  interpolation_scale=interpolation,
+                                                  dropout=self.dropout
+                                                  )
+                                     )
+            last_shape = self.de_conv_list[-1].shape
 
-        self.deconv2 = DeConvModule(self.deconv1.shape, conv_filters=self.filters[1],
-                                    conv_kernel=3,
-                                    conv_padding=1,
-                                    conv_stride=1,
-                                    normalize=use_norm,
-                                    activation=self.activation,
-                                    interpolation_scale=2,
-                                    dropout=self.dropout
-                                    )
-
-        self.deconv3 = DeConvModule(self.deconv2.shape, conv_filters=self.filters[2],
-                                    conv_kernel=3,
-                                    conv_padding=1,
-                                    conv_stride=1,
-                                    normalize=use_norm,
-                                    activation=self.activation,
-                                    interpolation_scale=2,
-                                    dropout=self.dropout
-                                    )
-
-        self.deconv4 = DeConvModule(self.deconv3.shape, conv_filters=out_channels,
-                                    conv_kernel=3,
-                                    conv_padding=1,
-                                    # normalize=norm,
-                                    activation=self.out_activation
-                                    )
+        self.de_conv_out = DeConvModule(self.de_conv_list[-1].shape, conv_filters=out_channels, conv_kernel=3,
+                                        conv_padding=1, activation=self.out_activation
+                                        )
 
     def forward(self, z):
         tensor = self.l1(z)
-        tensor = self.inner_activation(tensor)
+        tensor = self.activation(tensor)
         tensor = self.flat(tensor)
-        tensor = self.deconv1(tensor)
-        tensor = self.deconv2(tensor)
-        tensor = self.deconv3(tensor)
-        tensor = self.deconv4(tensor)
+
+        for de_conv in self.de_conv_list:
+            tensor = de_conv(tensor)
+
+        tensor = self.de_conv_out(tensor)
         return tensor
 
     def size(self):
@@ -114,18 +178,17 @@ class UnitGenerator(Generator):
         return tensor
 
 
-class BaseEncoder(ShapeMixin, nn.Module):
+class BaseCNNEncoder(ShapeMixin, nn.Module):
 
     # noinspection PyUnresolvedReferences
     def __init__(self, in_shape, lat_dim=256, use_bias=True, use_norm=False, dropout: Union[int, float] = 0,
                  latent_activation: Union[nn.Module, None] = None, activation: nn.Module = nn.ELU,
-                 filters: List[int] = None):
-        super(BaseEncoder, self).__init__()
-        assert filters, '"Filters" has to be a list of int len 3'
-
-        # Optional Padding for odd image-sizes
-        # Obsolet, already Done by autopadding module on incoming tensors
-        # in_shape = [x+1 if x % 2 != 0 and idx else x for idx, x in enumerate(in_shape)]
+                 filters: List[int] = None, kernels: Union[List[int], int, None] = None, **kwargs):
+        super(BaseCNNEncoder, self).__init__()
+        assert filters, '"Filters" has to be a list of int'
+        kernels = kernels or [3] * len(filters)
+        kernels = kernels if not isinstance(kernels, int) else [kernels] * len(filters)
+        assert len(kernels) == len(filters), 'Length of "Filters" and "Kernels" has to be same.'
 
         # Parameters
         self.lat_dim = lat_dim
@@ -133,52 +196,39 @@ class BaseEncoder(ShapeMixin, nn.Module):
         self.use_bias = use_bias
         self.latent_activation = latent_activation() if latent_activation else None
 
+        self.conv_list = nn.ModuleList()
+
         # Modules
-        self.conv1 = ConvModule(self.in_shape, conv_filters=filters[0],
-                                conv_kernel=3,
-                                conv_padding=1,
-                                conv_stride=1,
-                                pooling_size=2,
-                                use_norm=use_norm,
-                                dropout=dropout,
-                                activation=activation
-                                )
+        last_shape = self.in_shape
+        for conv_filter, conv_kernel in zip(filters, kernels):
+            self.conv_list.append(ConvModule(last_shape, conv_filters=conv_filter,
+                                             conv_kernel=conv_kernel,
+                                             conv_padding=conv_kernel-2,
+                                             conv_stride=1,
+                                             pooling_size=2,
+                                             use_norm=use_norm,
+                                             dropout=dropout,
+                                             activation=activation
+                                             )
+                                  )
+            last_shape = self.conv_list[-1].shape
+        self.last_conv_shape = last_shape
 
-        self.conv2 = ConvModule(self.conv1.shape, conv_filters=filters[1],
-                                conv_kernel=3,
-                                conv_padding=1,
-                                conv_stride=1,
-                                pooling_size=2,
-                                use_norm=use_norm,
-                                dropout=dropout,
-                                activation=activation
-                                )
-
-        self.conv3 = ConvModule(self.conv2.shape, conv_filters=filters[2],
-                                conv_kernel=5,
-                                conv_padding=2,
-                                conv_stride=1,
-                                pooling_size=2,
-                                use_norm=use_norm,
-                                dropout=dropout,
-                                activation=activation
-                                )
-
-        self.flat = Flatten()
+        self.flat = Flatten(self.last_conv_shape)
 
     def forward(self, x):
-        tensor = self.conv1(x)
-        tensor = self.conv2(tensor)
-        tensor = self.conv3(tensor)
+        tensor = x
+        for conv in self.conv_list:
+            tensor = conv(tensor)
         tensor = self.flat(tensor)
         return tensor
 
 
-class UnitEncoder(BaseEncoder):
+class UnitCNNEncoder(BaseCNNEncoder):
     # noinspection PyUnresolvedReferences
     def __init__(self, *args, **kwargs):
         kwargs.update(use_norm=True)
-        super(UnitEncoder, self).__init__(*args, **kwargs)
+        super(UnitCNNEncoder, self).__init__(*args, **kwargs)
         self.l1 = nn.Linear(reduce(mul, self.conv3.shape), self.lat_dim, bias=self.use_bias)
 
     def forward(self, x):
@@ -190,10 +240,10 @@ class UnitEncoder(BaseEncoder):
         return c1, c2, c3, l1
 
 
-class VariationalEncoder(BaseEncoder):
+class VariationalCNNEncoder(BaseCNNEncoder):
     # noinspection PyUnresolvedReferences
     def __init__(self, *args, **kwargs):
-        super(VariationalEncoder, self).__init__(*args, **kwargs)
+        super(VariationalCNNEncoder, self).__init__(*args, **kwargs)
 
         self.logvar = nn.Linear(reduce(mul, self.conv3.shape), self.lat_dim, bias=self.use_bias)
         self.mu = nn.Linear(reduce(mul, self.conv3.shape), self.lat_dim, bias=self.use_bias)
@@ -205,22 +255,22 @@ class VariationalEncoder(BaseEncoder):
         return mu + eps*std
 
     def forward(self, x):
-        tensor = super(VariationalEncoder, self).forward(x)
+        tensor = super(VariationalCNNEncoder, self).forward(x)
         mu = self.mu(tensor)
         logvar = self.logvar(tensor)
         z = self.reparameterize(mu, logvar)
         return mu, logvar, z
 
 
-class Encoder(BaseEncoder):
-    # noinspection PyUnresolvedReferences
-    def __init__(self, *args, **kwargs):
-        super(Encoder, self).__init__(*args, **kwargs)
+class CNNEncoder(BaseCNNEncoder):
 
-        self.l1 = nn.Linear(reduce(mul, self.conv3.shape), self.lat_dim, bias=self.use_bias)
+    def __init__(self, *args, **kwargs):
+        super(CNNEncoder, self).__init__(*args, **kwargs)
+
+        self.l1 = nn.Linear(self.flat.shape, self.lat_dim, bias=self.use_bias)
 
     def forward(self, x):
-        tensor = super(Encoder, self).forward(x)
+        tensor = super(CNNEncoder, self).forward(x)
         tensor = self.l1(tensor)
         tensor = self.latent_activation(tensor) if self.latent_activation else tensor
         return tensor

modules/util.py

@@ -0,0 +1,420 @@
+from functools import reduce
+from matplotlib import pyplot as plt
+
+from abc import ABC
+from pathlib import Path
+
+import torch
+from operator import mul
+from pytorch_lightning.utilities import argparse_utils
+from torch import nn
+from torch.nn import functional as F, Unfold
+
+from sklearn.metrics import ConfusionMatrixDisplay
+
+# Utility - Modules
+###################
+from ..metrics.binary_class_classifictaion import BinaryScores
+from ..metrics.multi_class_classification import MultiClassScores
+from ..utils.model_io import ModelParameters
+from ..utils.tools import add_argparse_args
+
+try:
+    import pytorch_lightning as pl
+
+    class PLMetrics(pl.metrics.Metric):
+
+        def __init__(self, n_classes, tag=''):
+            super(PLMetrics, self).__init__()
+
+            self.n_classes = n_classes
+            self.tag = tag
+
+            self.accuracy_score = pl.metrics.Accuracy(compute_on_step=False,)
+            self.precision = pl.metrics.Precision(num_classes=self.n_classes, average='macro', compute_on_step=False,
+                                                  is_multiclass=True)
+            self.recall = pl.metrics.Recall(num_classes=self.n_classes, average='macro', compute_on_step=False,
+                                            is_multiclass=True)
+            self.confusion_matrix = pl.metrics.ConfusionMatrix(self.n_classes, normalize='true', compute_on_step=False)
+            # self.precision_recall_curve = pl.metrics.PrecisionRecallCurve(self.n_classes, compute_on_step=False)
+            # self.average_prec = pl.metrics.AveragePrecision(self.n_classes, compute_on_step=True)
+            # self.roc = pl.metrics.ROC(self.n_classes, compute_on_step=False)
+            if self.n_classes > 2:
+                self.fbeta = pl.metrics.FBeta(self.n_classes, average='macro', compute_on_step=False)
+                self.f1 = pl.metrics.F1(self.n_classes, average='macro', compute_on_step=False)
+
+        def __iter__(self):
+            return iter(((name, metric) for name, metric in self._modules.items()))
+
+        def update(self, preds, target) -> None:
+            for _, metric in self:
+                try:
+                    if self.n_classes <= 2:
+                        metric.update(preds, target)
+                    else:
+                        metric.update(preds, target)
+                except ValueError:
+                    print(f'error was: {ValueError}')
+                    print(f'Metric is: {metric}')
+                    print(f'Shape is: preds - {preds.squeeze().shape}, target - {target.shape}')
+                    metric.update(preds.squeeze(), target)
+                except AssertionError:
+                    print(f'error was: {AssertionError}')
+                    print(f'Metric is: {metric}')
+                    print(f'Shape is: preds - {preds.shape}, target - {target.unsqueeze(-1).shape}')
+                    metric.update(preds, target.unsqueeze(-1))
+
+        def reset(self) -> None:
+            for _, metric in self:
+                metric.reset()
+
+        def compute(self) -> dict:
+            tag = f'{self.tag}_' if self.tag else ''
+            return {f'{tag}{metric_name}_score': metric.compute() for metric_name, metric in self}
+
+        def compute_and_prepare(self):
+            pl_metrics = self.compute()
+            images_from_metrics = dict()
+            for metric_name in list(pl_metrics.keys()):
+                if 'curve' in metric_name:
+                    continue
+                    roc_curve = pl_metrics.pop(metric_name)
+                    print('debug_point')
+
+                elif 'matrix' in metric_name:
+                    matrix = pl_metrics.pop(metric_name)
+                    fig1, ax1 = plt.subplots(dpi=96)
+                    disp = ConfusionMatrixDisplay(confusion_matrix=matrix.cpu().numpy(),
+                                                  display_labels=[i for i in range(self.n_classes)]
+                                                  )
+                    disp.plot(include_values=True, ax=ax1)
+                    images_from_metrics[metric_name] = fig1
+
+                elif 'ROC' in metric_name:
+                    continue
+                    roc = pl_metrics.pop(metric_name)
+                    print('debug_point')
+                else:
+                    pl_metrics[metric_name] = pl_metrics[metric_name].cpu().item()
+
+            return pl_metrics, images_from_metrics
+
+
+    class LightningBaseModule(pl.LightningModule, ABC):
+
+        @classmethod
+        def name(cls):
+            return cls.__name__
+
+        @property
+        def shape(self):
+            try:
+                x = torch.randn(self.in_shape).unsqueeze(0)
+                output = self(x)
+                return output.shape[1:]
+            except Exception as e:
+                print(e)
+                return -1
+
+        @classmethod
+        def from_argparse_args(cls, args, **kwargs):
+            return argparse_utils.from_argparse_args(cls, args, **kwargs)
+
+        @classmethod
+        def add_argparse_args(cls, parent_parser):
+            return add_argparse_args(cls, parent_parser)
+
+        def __init__(self, model_parameters, weight_init='xavier_normal_'):
+            super(LightningBaseModule, self).__init__()
+            self._weight_init = weight_init
+            self.params = ModelParameters(model_parameters)
+
+            if hasattr(self.params, 'n_classes'):
+                self.metrics = PLMetrics(self.params.n_classes, tag='PL')
+            else:
+                pass
+
+        def size(self):
+            return self.shape
+
+        def save_to_disk(self, model_path):
+            Path(model_path, exist_ok=True).mkdir(parents=True, exist_ok=True)
+            if not (model_path / 'model_class.obj').exists():
+                with (model_path / 'model_class.obj').open('wb') as f:
+                    torch.save(self.__class__, f)
+            return True
+
+        @property
+        def data_len(self):
+            return len(self.dataset.train_dataset)
+
+        @property
+        def n_train_batches(self):
+            return len(self.train_dataloader())
+
+        def configure_optimizers(self):
+            raise NotImplementedError
+
+        def forward(self, *args, **kwargs):
+            raise NotImplementedError
+
+        def training_step(self, batch_xy, batch_nb, *args, **kwargs):
+            raise NotImplementedError
+
+        def test_step(self, *args, **kwargs):
+            raise NotImplementedError
+
+        def test_epoch_end(self, outputs):
+            raise NotImplementedError
+
+        def init_weights(self):
+            if isinstance(self._weight_init, str):
+                mod = __import__('torch.nn.init', fromlist=[self._weight_init])
+                self._weight_init = getattr(mod, self._weight_init)
+            assert callable(self._weight_init)
+            weight_initializer = WeightInit(in_place_init_function=self._weight_init)
+            self.apply(weight_initializer)
+
+        def additional_scores(self, outputs):
+            if self.params.n_classes > 2:
+                return MultiClassScores(self)(outputs)
+            else:
+                return BinaryScores(self)(outputs)
+
+    module_types = (LightningBaseModule, nn.Module,)
+
+except ImportError:
+    module_types = (nn.Module,)
+    pl = None
+    pass  # Maybe post a hint to install pytorch-lightning.
+
+
+class ShapeMixin:
+
+    @property
+    def shape(self):
+
+        assert isinstance(self, module_types)
+
+        def get_out_shape(output):
+            return output.shape[1:] if len(output.shape[1:]) > 1 else output.shape[-1]
+
+        in_shape = self.in_shape if hasattr(self, 'in_shape') else None
+        if in_shape is not None:
+            try:
+                device = self.device
+            except AttributeError:
+                try:
+                    device = next(self.parameters()).device
+                except StopIteration:
+                    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+            x = torch.randn(in_shape, device=device)
+            # This is needed for BatchNorm shape checking
+            x = torch.stack((x, x))
+
+            # noinspection PyCallingNonCallable
+            y = self(x)
+            if isinstance(y, tuple):
+                shape = tuple([get_out_shape(y[i]) for i in range(len(y))])
+            else:
+                shape = get_out_shape(y)
+            return shape
+        else:
+            return -1
+
+    @property
+    def flat_shape(self):
+        shape = self.shape
+        try:
+            return reduce(mul, shape)
+        except TypeError:
+            return shape
+
+
+class F_x(ShapeMixin, nn.Identity):
+    def __init__(self, in_shape):
+        super(F_x, self).__init__()
+        self.in_shape = in_shape
+
+
+class SlidingWindow(ShapeMixin, nn.Module):
+    def __init__(self, in_shape, kernel, stride=1, padding=0, keepdim=False):
+        super(SlidingWindow, self).__init__()
+        self.in_shape = in_shape
+        self.kernel = kernel if not isinstance(kernel, int) else (kernel, kernel)
+        self.padding = padding
+        self.stride = stride
+        self.keepdim = keepdim
+        self._unfolder = Unfold(self.kernel, dilation=1, padding=self.padding, stride=self.stride)
+
+    def forward(self, x):
+        tensor = self._unfolder(x)
+        tensor = tensor.transpose(-1, -2)
+        if self.keepdim:
+            shape = *x.shape[:2], -1, *self.kernel
+            tensor = tensor.reshape(shape)
+        return tensor
+
+
+# Utility - Modules
+###################
+class Flatten(ShapeMixin, nn.Module):
+
+    def __init__(self, in_shape, to=-1):
+        assert isinstance(to, int) or isinstance(to, tuple)
+        super(Flatten, self).__init__()
+        self.in_shape = in_shape
+        self.to = (to,) if isinstance(to, int) else to
+
+    def forward(self, x):
+        return x.view(x.size(0), *self.to)
+
+
+class Interpolate(nn.Module):
+    def __init__(self,  size=None, scale_factor=None, mode='nearest', align_corners=None):
+        super(Interpolate, self).__init__()
+        self.interp = nn.functional.interpolate
+        self.size = size
+        self.scale_factor = scale_factor
+        self.align_corners = align_corners
+        self.mode = mode
+
+    def forward(self, x):
+        x = self.interp(x, size=self.size, scale_factor=self.scale_factor,
+                        mode=self.mode, align_corners=self.align_corners)
+        return x
+
+
+class AutoPad(nn.Module):
+
+    def __init__(self, interpolations=3, base=2):
+        super(AutoPad, self).__init__()
+        self.fct = base ** interpolations
+
+    def forward(self, x):
+        # noinspection PyUnresolvedReferences
+        x = F.pad(x,
+                  [0,
+                   (x.shape[-1] // self.fct + 1) * self.fct - x.shape[-1] if x.shape[-1] % self.fct != 0 else 0,
+                   (x.shape[-2] // self.fct + 1) * self.fct - x.shape[-2] if x.shape[-2] % self.fct != 0 else 0,
+                   0])
+        return x
+
+
+class WeightInit:
+
+    def __init__(self, in_place_init_function):
+        self.in_place_init_function = in_place_init_function
+
+    def __call__(self, m):
+        if hasattr(m, 'weight'):
+            if isinstance(m.weight, torch.Tensor):
+                if m.weight.ndim < 2:
+                    m.weight.data.fill_(0.01)
+                else:
+                    self.in_place_init_function(m.weight)
+        if hasattr(m, 'bias'):
+            if isinstance(m.bias, torch.Tensor):
+                m.bias.data.fill_(0.01)
+
+
+class Filter(nn.Module, ShapeMixin):
+
+    def __init__(self, in_shape, pos, dim=-1):
+        super(Filter, self).__init__()
+
+        self.in_shape = in_shape
+        self.pos = pos
+        self.dim = dim
+        raise SystemError('Do not use this Module - broken.')
+
+    @staticmethod
+    def forward(x):
+        tensor = x[:, -1]
+        return tensor
+
+
+class FlipTensor(nn.Module):
+    def __init__(self, dim=-2):
+        super(FlipTensor, self).__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        idx = [i for i in range(x.size(self.dim) - 1, -1, -1)]
+        idx = torch.as_tensor(idx).long()
+        inverted_tensor = x.index_select(self.dim, idx)
+        return inverted_tensor
+
+
+class AutoPadToShape(nn.Module):
+    def __init__(self, target_shape):
+        super(AutoPadToShape, self).__init__()
+        self.target_shape = target_shape
+
+    def forward(self, x):
+        if not torch.is_tensor(x):
+            x = torch.as_tensor(x)
+        if x.shape[-len(self.target_shape):] == self.target_shape or x.shape == self.target_shape:
+            return x
+
+        idx = [0] * (len(self.target_shape) * 2)
+        for i, j in zip(range(-1, -(len(self.target_shape)+1), -1), range(0, len(idx), 2)):
+            idx[j] = self.target_shape[i] - x.shape[i]
+        x = torch.nn.functional.pad(x, idx)
+        return x
+
+    def __repr__(self):
+        return f'AutoPadTransform({self.target_shape})'
+
+
+class Splitter(nn.Module):
+
+    @property
+    def shape(self):
+        return tuple([self._out_shape] * self.n)
+
+    @property
+    def out_shape(self):
+        return self._out_shape
+
+    def __init__(self, in_shape, n, dim=-1):
+        super(Splitter, self).__init__()
+
+        self.in_shape = (in_shape, ) if isinstance(in_shape, int) else in_shape
+        self.n = n
+        self.dim = dim if dim > 0 else len(self.in_shape) - abs(dim)
+
+        self.new_dim_size = (self.in_shape[self.dim] // self.n) + (1 if self.in_shape[self.dim] % self.n != 0 else 0)
+        self._out_shape = tuple([x if self.dim != i else self.new_dim_size for i, x in enumerate(self.in_shape)])
+
+        self.autopad = AutoPadToShape(self._out_shape)
+
+    def forward(self, x: torch.Tensor):
+        dim = self.dim + 1 if len(self.in_shape) == (x.ndim - 1) else self.dim
+        x = x.transpose(0, dim)
+        n_blocks = list()
+        for block_idx in range(self.n):
+            start = block_idx * self.new_dim_size
+            end = (block_idx + 1) * self.new_dim_size
+            block = x[start:end].transpose(0, dim)
+            block = self.autopad(block)
+            n_blocks.append(block)
+        return n_blocks
+
+
+class Merger(nn.Module, ShapeMixin):
+
+    @property
+    def shape(self):
+        y = self.forward([torch.randn(self.in_shape) for _ in range(self.n)])
+        return y.shape
+
+    def __init__(self, in_shape, n, dim=-1):
+        super(Merger, self).__init__()
+
+        self.n = n
+        self.dim = dim
+        self.in_shape = in_shape
+
+    def forward(self, x):
+        return torch.cat(x, dim=self.dim)

modules/utils.py

@@ -1,256 +0,0 @@
-from abc import ABC
-from pathlib import Path
-
-import torch
-from torch import nn
-from torch import functional as F
-
-import pytorch_lightning as pl
-
-
-# Utility - Modules
-###################
-from utils.model_io import ModelParameters
-
-
-class ShapeMixin:
-
-    @property
-    def shape(self):
-        assert isinstance(self, (LightningBaseModule, nn.Module))
-        if self.in_shape is not None:
-            x = torch.randn(self.in_shape)
-            # This is needed for BatchNorm shape checking
-            x = torch.stack((x, x))
-            output = self(x)
-            return output.shape[1:] if len(output.shape[1:]) > 1 else output.shape[-1]
-        else:
-            return -1
-
-
-class F_x(ShapeMixin, nn.Module):
-    def __init__(self, in_shape):
-        super(F_x, self).__init__()
-        self.in_shape = in_shape
-
-    def forward(self, x):
-        return x
-
-
-# Utility - Modules
-###################
-class Flatten(ShapeMixin, nn.Module):
-
-    def __init__(self, in_shape, to=-1):
-        assert isinstance(to, int) or isinstance(to, tuple)
-        super(Flatten, self).__init__()
-        self.in_shape = in_shape
-        self.to = (to,) if isinstance(to, int) else to
-
-    def forward(self, x):
-        return x.view(x.size(0), *self.to)
-
-
-class Interpolate(nn.Module):
-    def __init__(self,  size=None, scale_factor=None, mode='nearest', align_corners=None):
-        super(Interpolate, self).__init__()
-        self.interp = nn.functional.interpolate
-        self.size = size
-        self.scale_factor = scale_factor
-        self.align_corners = align_corners
-        self.mode = mode
-
-    def forward(self, x):
-        x = self.interp(x, size=self.size, scale_factor=self.scale_factor,
-                        mode=self.mode, align_corners=self.align_corners)
-        return x
-
-
-class AutoPad(nn.Module):
-
-    def __init__(self, interpolations=3, base=2):
-        super(AutoPad, self).__init__()
-        self.fct = base ** interpolations
-
-    def forward(self, x):
-        # noinspection PyUnresolvedReferences
-        x = F.pad(x,
-                  [0,
-                   (x.shape[-1] // self.fct + 1) * self.fct - x.shape[-1] if x.shape[-1] % self.fct != 0 else 0,
-                   (x.shape[-2] // self.fct + 1) * self.fct - x.shape[-2] if x.shape[-2] % self.fct != 0 else 0,
-                   0])
-        return x
-
-
-class WeightInit:
-
-    def __init__(self, in_place_init_function):
-        self.in_place_init_function = in_place_init_function
-
-    def __call__(self, m):
-        if hasattr(m, 'weight'):
-            if isinstance(m.weight, torch.Tensor):
-                if m.weight.ndim < 2:
-                    m.weight.data.fill_(0.01)
-                else:
-                    self.in_place_init_function(m.weight)
-        if hasattr(m, 'bias'):
-            if isinstance(m.bias, torch.Tensor):
-                m.bias.data.fill_(0.01)
-
-
-class LightningBaseModule(pl.LightningModule, ABC):
-
-    @classmethod
-    def name(cls):
-        return cls.__name__
-
-    @property
-    def shape(self):
-        try:
-            x = torch.randn(self.in_shape).unsqueeze(0)
-            output = self(x)
-            return output.shape[1:]
-        except Exception as e:
-            print(e)
-            return -1
-
-    def __init__(self, hparams):
-        super(LightningBaseModule, self).__init__()
-
-        # Set Parameters
-        ################################
-        self.hparams = hparams
-        self.params = ModelParameters(hparams)
-
-        # Dataset Loading
-        ################################
-        # TODO: Find a way to push Class Name, library path and parameters (sometimes thiose are objects) in here
-
-    def size(self):
-        return self.shape
-
-    def save_to_disk(self, model_path):
-        Path(model_path, exist_ok=True).mkdir(parents=True, exist_ok=True)
-        if not (model_path / 'model_class.obj').exists():
-            with (model_path / 'model_class.obj').open('wb') as f:
-                torch.save(self.__class__, f)
-        return True
-
-    @property
-    def data_len(self):
-        return len(self.dataset.train_dataset)
-
-    @property
-    def n_train_batches(self):
-        return len(self.train_dataloader())
-
-    def configure_optimizers(self):
-        raise NotImplementedError
-
-    def forward(self, *args, **kwargs):
-        raise NotImplementedError
-
-    def training_step(self, batch_xy, batch_nb, *args, **kwargs):
-        raise NotImplementedError
-
-    def test_step(self, *args, **kwargs):
-        raise NotImplementedError
-
-    def test_epoch_end(self, outputs):
-        raise NotImplementedError
-
-    def init_weights(self, in_place_init_func_=nn.init.xavier_uniform_):
-        weight_initializer = WeightInit(in_place_init_function=in_place_init_func_)
-        self.apply(weight_initializer)
-
-
-class FilterLayer(nn.Module):
-
-    def __init__(self):
-        super(FilterLayer, self).__init__()
-
-    def forward(self, x):
-        tensor = x[:, -1]
-        return tensor
-
-
-class MergingLayer(nn.Module):
-
-    def __init__(self):
-        super(MergingLayer, self).__init__()
-
-    def forward(self, x):
-        # ToDo: Which ones to combine?
-        return
-
-
-class FlipTensor(nn.Module):
-    def __init__(self, dim=-2):
-        super(FlipTensor, self).__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        idx = [i for i in range(x.size(self.dim) - 1, -1, -1)]
-        idx = torch.as_tensor(idx).long()
-        inverted_tensor = x.index_select(self.dim, idx)
-        return inverted_tensor
-
-
-class AutoPadToShape(object):
-    def __init__(self, shape):
-        self.shape = shape
-
-    def __call__(self, x):
-        if not torch.is_tensor(x):
-            x = torch.as_tensor(x)
-        if x.shape[1:] == self.shape:
-            return x
-        embedding = torch.zeros((x.shape[0], *self.shape))
-        embedding[:, :x.shape[1], :x.shape[2], :x.shape[3]] = x
-        return embedding
-
-    def __repr__(self):
-        return f'AutoPadTransform({self.shape})'
-
-
-class HorizontalSplitter(nn.Module):
-
-    def __init__(self, in_shape, n):
-        super(HorizontalSplitter, self).__init__()
-        assert len(in_shape) == 3
-        self.n = n
-        self.in_shape = in_shape
-
-        self.channel, self.height, self.width = self.in_shape
-        self.new_height = (self.height // self.n) + (1 if self.height % self.n != 0 else 0)
-
-        self.shape = (self.channel, self.new_height, self.width)
-        self.autopad = AutoPadToShape(self.shape)
-
-    def forward(self, x):
-        n_blocks = list()
-        for block_idx in range(self.n):
-            start = block_idx * self.new_height
-            end = (block_idx + 1) * self.new_height
-            block = self.autopad(x[:, :, start:end, :])
-            n_blocks.append(block)
-
-        return n_blocks
-
-
-class HorizontalMerger(nn.Module):
-
-    @property
-    def shape(self):
-        merged_shape = self.in_shape[0], self.in_shape[1] * self.n, self.in_shape[2]
-        return merged_shape
-
-    def __init__(self, in_shape, n):
-        super(HorizontalMerger, self).__init__()
-        assert len(in_shape) == 3
-        self.n = n
-        self.in_shape = in_shape
-
-    def forward(self, x):
-        return torch.cat(x, dim=-2)

point_toolset/point_io.py

@@ -0,0 +1,37 @@
+import torch
+from torch_geometric.data import Data
+
+
+class BatchToData(object):
+    def __init__(self, transforms=None):
+        super(BatchToData, self).__init__()
+        self.transforms = transforms if transforms else lambda x: x
+
+    def __call__(self, batch_dict):
+        # Convert to torch_geometric.data.Data type
+
+        batch_pos = batch_dict['pos']
+        batch_norm = batch_dict.get('norm', None)
+        batch_y = batch_dict.get('y', None)
+        batch_y_c = batch_dict.get('y_c', None)
+
+        batch_size, num_points, _ = batch_pos.shape  # (batch_size, num_points, 3)
+
+        batch_size, N, _ = batch_pos.shape  # (batch_size, num_points, 3)
+        pos = batch_pos.view(batch_size * N, -1)
+        norm = batch_norm.view(batch_size * N, -1) if batch_norm is not None else batch_norm
+
+        batch_y_l = batch_y.view(batch_size * N, -1) if batch_y is not None else batch_y
+        batch_y_c = batch_y_c.view(batch_size * N, -1) if batch_y_c is not None else batch_y_c
+
+        batch = torch.zeros((batch_size, num_points), device=pos.device, dtype=torch.long)
+        for i in range(batch_size):
+            batch[i] = i
+        batch = batch.view(-1)
+
+        data = Data()
+        data.norm, data.pos, data.batch, data.yl, data.yc = norm, pos, batch, batch_y_l, batch_y_c
+
+        data = self.transforms(data)
+
+        return data

point_toolset/point_transforms.py

@@ -0,0 +1,26 @@
+import torch
+from torch_geometric.transforms import NormalizeScale
+
+
+class NormalizePositions(NormalizeScale):
+
+    def __init__(self):
+        super(NormalizePositions, self).__init__()
+
+    def __call__(self, data):
+        if torch.isnan(data.pos).any():
+            print('debug')
+
+        data = self.center(data)
+        if torch.isnan(data.pos).any():
+            print('debug')
+
+        scale = (1 / data.pos.abs().max()) * 0.999999
+        if torch.isnan(scale).any() or torch.isinf(scale).any():
+            print('debug')
+
+        data.pos = data.pos * scale
+        if torch.isnan(data.pos).any():
+            print('debug')
+
+        return data

point_toolset/sampling.py

@@ -0,0 +1,51 @@
+from abc import ABC
+
+import numpy as np
+
+
+class _Sampler(ABC):
+
+    def __init__(self, K, **kwargs):
+        self.k = K
+        self.kwargs = kwargs
+
+    def __call__(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class RandomSampling(_Sampler):
+
+    def __init__(self, *args, **kwargs):
+        super(RandomSampling, self).__init__(*args, **kwargs)
+
+    def __call__(self, pts, *args, **kwargs):
+        rnd_indexs = np.random.choice(np.arange(pts.shape[0]), min(self.k, pts.shape[0]), replace=False)
+        return rnd_indexs
+
+
+class FarthestpointSampling(_Sampler):
+
+    def __init__(self, *args, **kwargs):
+        super(FarthestpointSampling, self).__init__(*args, **kwargs)
+
+    @staticmethod
+    def calc_distances(p0, points):
+        return ((p0[:3] - points[:, :3]) ** 2).sum(axis=1)
+
+    def __call__(self, pts, *args, **kwargs):
+
+        if pts.shape[0] < self.k:
+            return pts
+
+        else:
+            farthest_pts = np.zeros((self.k, pts.shape[1]))
+            farthest_pts_idx = np.zeros(self.k, dtype=np.int)
+            farthest_pts[0] = pts[np.random.randint(len(pts))]
+            distances = self.calc_distances(farthest_pts[0], pts)
+            for i in range(1, self.k):
+                farthest_pts_idx[i] = np.argmax(distances)
+                farthest_pts[i] = pts[farthest_pts_idx[i]]
+
+                distances = np.minimum(distances, self.calc_distances(farthest_pts[i], pts))
+
+            return farthest_pts_idx

requirements.txt

@@ -1,92 +1,8 @@
-absl-py==0.9.0
-appdirs==1.4.3
-attrs==19.3.0
-audioread==2.1.8
-bravado==10.6.0
-bravado-core==5.17.0
-CacheControl==0.12.6
-cachetools==4.1.0
-certifi==2019.11.28
-cffi==1.14.0
-chardet==3.0.4
-click==7.1.2
-colorama==0.4.3
-contextlib2==0.6.0
-cycler==0.10.0
-decorator==4.4.2
-distlib==0.3.0
-distro==1.4.0
-future==0.18.2
-gitdb==4.0.5
-GitPython==3.1.2
-google-auth==1.14.3
-google-auth-oauthlib==0.4.1
-grpcio==1.29.0
-html5lib==1.0.1
-idna==2.8
-ipaddr==2.2.0
-joblib==0.15.1
-jsonpointer==2.0
-jsonref==0.2
-jsonschema==3.2.0
-kiwisolver==1.2.0
-librosa==0.7.2
-llvmlite==0.32.1
-lockfile==0.12.2
-Markdown==3.2.2
-matplotlib==3.2.1
-monotonic==1.5
-msgpack==0.6.2
-msgpack-python==0.5.6
 natsort==7.0.1
-neptune-client==0.4.113
-numba==0.49.1
-numpy==1.18.4
-oauthlib==3.1.0
-packaging==20.3
-pandas==1.0.3
-pep517==0.8.2
-Pillow==7.1.2
-progress==1.5
-protobuf==3.12.0
-py3nvml==0.2.6
-pyasn1==0.4.8
-pyasn1-modules==0.2.8
-pycparser==2.20
-PyJWT==1.7.1
-pyparsing==2.4.6
-pyrsistent==0.16.0
-python-dateutil==2.8.1
-pytoml==0.1.21
+neptune-client==0.4.109
 pytorch-lightning==0.7.6
-pytz==2020.1
-PyYAML==5.3.1
-requests==2.22.0
-requests-oauthlib==1.3.0
-resampy==0.2.2
-retrying==1.3.3
-rfc3987==1.3.8
-rsa==4.0
-scikit-learn==0.23.0
-scipy==1.4.1
-simplejson==3.17.0
-six==1.14.0
-smmap==3.0.4
-SoundFile==0.10.3.post1
-strict-rfc3339==0.7
-swagger-spec-validator==2.5.0
-tensorboard==2.2.1
-tensorboard-plugin-wit==1.6.0.post3
-threadpoolctl==2.0.0
-torch==1.5.0+cu101
-torchvision==0.6.0+cu101
-tqdm==4.46.0
-typing-extensions==3.7.4.2
-urllib3==1.25.8
-webcolors==1.11.1
-webencodings==0.5.1
-websocket-client==0.57.0
-Werkzeug==1.0.1
-xmltodict==0.12.0
-
-torchcontrib~=0.0.2
+test-tube==0.7.5
+torch==1.4.0
+torchcontrib==0.0.2
+torchvision==0.5.0
+tqdm==4.45.0

utils/_basedatamodule.py

@@ -0,0 +1,36 @@
+from pytorch_lightning import LightningDataModule
+
+
+# Dataset Options
+from ml_lib.utils.tools import add_argparse_args
+
+DATA_OPTION_test = 'test'
+DATA_OPTION_devel = 'devel'
+DATA_OPTION_train = 'train'
+DATA_OPTIONS = [DATA_OPTION_train, DATA_OPTION_devel, DATA_OPTION_test]
+
+
+class _BaseDataModule(LightningDataModule):
+
+    @property
+    def shape(self):
+        return self.datasets[DATA_OPTION_train].sample_shape
+
+    @classmethod
+    def add_argparse_args(cls, parent_parser):
+        return add_argparse_args(cls, parent_parser)
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.datasets = dict()
+
+    def transfer_batch_to_device(self, batch, device):
+        if isinstance(batch, list):
+            for idx, item in enumerate(batch):
+                try:
+                    batch[idx] = item.to(device)
+                except (AttributeError, RuntimeError):
+                    continue
+            return batch
+        else:
+            return batch.to(device)

utils/callbacks.py

@@ -0,0 +1,28 @@
+import torch
+from pytorch_lightning import Callback, Trainer, LightningModule
+
+
+class BestScoresCallback(Callback):
+
+    def __init__(self, *monitors) -> None:
+        super().__init__()
+        self.monitors = list(*monitors)
+
+        self.best_scores = {monitor: 0.0 for monitor in self.monitors}
+        self.best_epoch = {monitor: 0 for monitor in self.monitors}
+
+    def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
+        epoch = pl_module.current_epoch
+
+        for monitor in self.best_scores.keys():
+            current_score = trainer.callback_metrics.get(monitor)
+            if current_score is None:
+                pass
+            elif torch.isinf(current_score):
+                pass
+            elif torch.isnan(current_score):
+                pass
+            else:
+                self.best_scores[monitor] = max(self.best_scores[monitor], current_score)
+                if self.best_scores[monitor] == current_score:
+                    self.best_epoch[monitor] = max(self.best_epoch[monitor], epoch)

utils/config.py

@@ -1,13 +1,84 @@
 import ast
+import configparser
+from distutils.util import strtobool
+from pathlib import Path
+from typing import Mapping, Dict
+
+import torch
 from copy import deepcopy
 
 from abc import ABC
 
 from argparse import Namespace, ArgumentParser
 from collections import defaultdict
-from configparser import ConfigParser
-from pathlib import Path
+from configparser import ConfigParser, DuplicateSectionError
 import hashlib
+from pytorch_lightning import Trainer
+
+from ml_lib.utils.loggers import LightningLogger
+from ml_lib.utils.tools import locate_and_import_class, auto_cast
+
+
+# Argument Parser and default Values
+# =============================================================================
+def parse_comandline_args_add_defaults(filepath, overrides=None):
+
+    # Parse Command Line
+    parser = ArgumentParser()
+    parser.add_argument('--model_name', type=str)
+    parser.add_argument('--data_name', type=str)
+    parser.add_argument('--seed', type=str)
+    parser.add_argument('--debug', type=strtobool)
+
+    # Load Defaults from _parameters.ini file
+    config = configparser.ConfigParser()
+    config.read(str(filepath))
+
+    new_defaults = dict()
+    for key in ['project', 'train', 'data']:
+        defaults = config[key]
+        new_defaults.update({key: auto_cast(val) for key, val in defaults.items()})
+
+    args, _ = parser.parse_known_args()
+    overrides = overrides or dict()
+    default_data = overrides.get('data_name', None) or new_defaults['data_name']
+    default_model = overrides.get('model_name', None) or new_defaults['model_name']
+    default_seed = overrides.get('seed', None) or new_defaults['seed']
+
+    data_name = args.__dict__.get('data_name', None) or default_data
+    model_name = args.__dict__.get('model_name', None) or default_model
+    found_seed = args.__dict__.get('seed', None) or default_seed
+
+    new_defaults.update({key: auto_cast(val) for key, val in config[model_name].items()})
+
+    found_data_class = locate_and_import_class(data_name, 'datasets')
+    found_model_class = locate_and_import_class(model_name, 'models')
+
+    for module in [LightningLogger, Trainer, found_data_class, found_model_class]:
+        parser = module.add_argparse_args(parser)
+
+    args, _ = parser.parse_known_args(namespace=Namespace(**new_defaults))
+
+    args = vars(args)
+    args.update({key: auto_cast(val) for key, val in args.items()})
+    args.update(gpus=[0] if torch.cuda.is_available() and not args['debug'] else None,
+                row_log_interval=1000,  # TODO: Better Value / Setting
+                log_save_interval=10000,  # TODO: Better Value / Setting
+                weights_summary='top',
+                )
+
+    if overrides is not None and isinstance(overrides, (Mapping, Dict)):
+        args.update(**overrides)
+    if args['debug']:
+        args.update(
+            # The seems to be the new "fast_dev_run"
+            val_check_interval=1,
+            max_epochs=2,
+            max_steps=2,
+            auto_lr_find=False,
+            check_val_every_n_epoch=1
+        )
+    return args, found_data_class, found_model_class, found_seed
 
 
 def is_jsonable(x):
@@ -38,11 +109,32 @@ class Config(ConfigParser, ABC):
     def fingerprint(self):
         h = hashlib.md5()
         params = deepcopy(self.as_dict)
-        del params['model']['type']
-        del params['model']['secondary_type']
-        del params['data']['worker']
-        del params['main']
-        h.update(str(params).encode())
+        try:
+            del params['model']['type']
+        except KeyError:
+            pass
+        try:
+            del params['data']['worker']
+        except KeyError:
+            pass
+        try:
+            del params['data']['refresh']
+        except KeyError:
+            pass
+        try:
+            del params['main']
+        except KeyError:
+            pass
+        try:
+            del params['project']
+        except KeyError:
+            pass
+        # Flatten the dict of dicts
+        for section in list(params.keys()):
+            params.update({f'{section}_{key}': val for key, val in params[section].items()})
+            del params[section]
+        _, vals = zip(*sorted(params.items(), key=lambda tup: tup[0]))
+        h.update(str(vals).encode())
         fingerprint = h.hexdigest()
         return fingerprint
 
@@ -53,6 +145,7 @@ class Config(ConfigParser, ABC):
 
     @property
     def _model_map(self):
+
         """
         This is function is supposed to return a dict, which holds a mapping from string model names to model classes
 
@@ -62,21 +155,31 @@ class Config(ConfigParser, ABC):
                     )
         :return:
         """
-
         raise NotImplementedError
 
     @property
     def model_class(self):
         try:
-            return self._model_map[self.model.type]
-        except KeyError:
-            raise KeyError(f'The model alias you provided ("{self.get("model", "type")}")' +
-                           'does not exist! Try one of these: {list(self._model_map.keys())}')
+            return locate_and_import_class(self.model.type, folder_path='models')
+        except AttributeError as e:
+            raise AttributeError(f'The model alias you provided ("{self.get("model", "type")}") ' +
+                                 f'was not found!\n' +
+                                 f'{e}')
 
+    @property
+    def data_class(self):
+        try:
+            return locate_and_import_class(self.data.class_name, folder_path='datasets')
+        except AttributeError as e:
+            raise AttributeError(f'The dataset alias you provided ("{self.get("data", "class_name")}") ' +
+                                 f'was not found!\n' +
+                                 f'{e}')
+
+    # --------------------------------------------------
     # TODO: Do this programmatically; This did not work:
     # Initialize Default Sections as Property
     # for section in self.default_sections:
-    #     self.__setattr__(section, property(lambda x :x._get_namespace_for_section(section))
+    #     self.__setattr__(section, property(lambda tensor :tensor._get_namespace_for_section(section))
 
     @property
     def main(self):
@@ -195,3 +298,22 @@ class Config(ConfigParser, ABC):
         with path.open('w') as configfile:
             super().write(configfile)
         return True
+
+    def _write_section(self, fp, section_name, section_items, delimiter):
+        if section_name == 'project':
+            return
+        else:
+            super(Config, self)._write_section(fp, section_name, section_items, delimiter)
+
+    def add_section(self, section: str) -> None:
+        try:
+            super(Config, self).add_section(section)
+        except DuplicateSectionError:
+            pass
+
+
+class DataClass(Namespace):
+
+    @property
+    def __dict__(self):
+        return [x for x in dir(self) if not x.startswith('_')]

utils/data_util.py

@@ -0,0 +1,41 @@
+import torch
+from torch.utils.data import Dataset
+
+
+def chunks(l, n):
+    """Yield successive n-sized chunks from l."""
+    for i in range(0, len(l), n):
+        yield l[i:i + n]
+
+
+class ReMapDataset(Dataset):
+    @property
+    def sample_shape(self):
+        return list(self[0][0].shape)
+
+    def __init__(self, ds, mapping):
+        super(ReMapDataset, self).__init__()
+        # here is a mapping from this index to the mother ds index
+        self.mapping = mapping
+        self.ds = ds
+
+    def __getitem__(self, index):
+        return self.ds[self.mapping[index]]
+
+    def __len__(self):
+        return self.mapping.shape[0]
+
+    @classmethod
+    def do_train_vali_split(cls, ds, split_fold=0.1):
+
+        indices = torch.randperm(len(ds))
+
+        valid_size = int(len(ds) * split_fold)
+
+        train_mapping = indices[valid_size:]
+        valid_mapping = indices[:valid_size]
+
+        train = cls(ds, train_mapping)
+        valid = cls(ds, valid_mapping)
+
+        return train, valid

utils/equal_sampler.py

@@ -0,0 +1,30 @@
+
+import random
+from typing import Iterator, Sequence
+
+from torch.utils.data import Sampler
+from torch.utils.data.sampler import T_co
+
+
+# noinspection PyMissingConstructor
+class EqualSampler(Sampler):
+
+    def __init__(self, idxs_per_class: Sequence[Sequence[float]], replacement: bool = True) -> None:
+
+        self.replacement = replacement
+        self.idxs_per_class = idxs_per_class
+        self.len_largest_class = max([len(x) for x in self.idxs_per_class])
+
+    def __iter__(self) -> Iterator[T_co]:
+        return iter(random.choice(self.idxs_per_class[random.randint(0, len(self.idxs_per_class)-1)])
+                    for _ in range(len(self)))
+
+    def __len__(self):
+        return self.len_largest_class * len(self.idxs_per_class)
+
+
+if __name__ == '__main__':
+    es = EqualSampler([list(range(5)), list(range(5, 10)), list(range(10, 12))])
+    for i in es:
+        print(i)
+    pass

utils/loggers.py

@@ -0,0 +1,185 @@
+import inspect
+from argparse import ArgumentParser
+from copy import deepcopy
+
+import hashlib
+from pathlib import Path
+
+import os
+from pytorch_lightning.loggers.base import LightningLoggerBase
+from neptune.api_exceptions import ProjectNotFound
+
+from pytorch_lightning.loggers.neptune import NeptuneLogger
+
+from pytorch_lightning.loggers.csv_logs import CSVLogger
+from pytorch_lightning.utilities import argparse_utils
+
+from ml_lib.utils.tools import add_argparse_args
+
+
+class LightningLogger(LightningLoggerBase):
+
+    @classmethod
+    def from_argparse_args(cls, args, **kwargs):
+        cleaned_args = deepcopy(args.__dict__)
+
+        # Clean Seed and other attributes
+        # TODO: Find a better way in cleaning this
+        for attr in ['seed', 'num_worker', 'debug', 'eval', 'owner', 'data_root', 'check_val_every_n_epoch',
+                     'reset', 'outpath', 'version', 'gpus', 'neptune_key', 'num_sanity_val_steps', 'tpu_cores',
+                     'progress_bar_refresh_rate', 'log_save_interval', 'row_log_interval']:
+
+            try:
+                del cleaned_args[attr]
+            except KeyError:
+                pass
+
+        kwargs.update(params=cleaned_args)
+        new_logger = argparse_utils.from_argparse_args(cls, args, **kwargs)
+        return new_logger
+
+    @property
+    def fingerprint(self):
+        h = hashlib.md5()
+        h.update(self._finger_print_string.encode())
+        fingerprint = h.hexdigest()
+        return fingerprint
+
+    @property
+    def name(self):
+        short_name = "".join(c for c in self.model_name if c.isupper())
+        return f'{short_name}_{self.fingerprint}'
+
+    media_dir = 'media'
+
+    @classmethod
+    def add_argparse_args(cls, parent_parser):
+        return add_argparse_args(cls, parent_parser)
+
+    @property
+    def experiment(self):
+        if self.debug:
+            return self.csvlogger.experiment
+        else:
+            return self.neptunelogger.experiment
+
+    @property
+    def log_dir(self):
+        return Path(self.csvlogger.experiment.log_dir)
+
+    @property
+    def project_name(self):
+        return f"{self.owner}/{self.projeect_root.replace('_', '-')}"
+
+    @property
+    def projeect_root(self):
+        root_path = Path(os.getcwd()).name if not self.debug else 'test'
+        return root_path
+
+    @property
+    def version(self):
+        return self.seed
+
+    @property
+    def save_dir(self):
+        return self.log_dir
+
+    @property
+    def outpath(self):
+        return Path(self.root_out) / self.model_name
+
+    def __init__(self, owner, neptune_key, model_name, outpath='output', seed=69, debug=False, params=None):
+        """
+        params (dict|None): Optional. Parameters of the experiment. After experiment creation params are read-only.
+           Parameters are displayed in the experiment’s Parameters section and each key-value pair can be
+           viewed in experiments view as a column.
+        properties (dict|None): Optional default is {}. Properties of the experiment.
+           They are editable after experiment is created. Properties are displayed in the experiment’s Details and
+           each key-value pair can be viewed in experiments view as a column.
+        tags (list|None): Optional default []. Must be list of str. Tags of the experiment.
+           They are editable after experiment is created (see: append_tag() and remove_tag()).
+           Tags are displayed in the experiment’s Details and can be viewed in experiments view as a column.
+        """
+        super(LightningLogger, self).__init__()
+
+        self.debug = debug
+        self.owner = owner if not self.debug else 'testuser'
+        self.neptune_key = neptune_key if not self.debug else 'XXX'
+        self.root_out = outpath if not self.debug else 'debug_out'
+
+        self.params = params
+
+        self.seed = seed
+        self.model_name = model_name
+
+        if self.params:
+            _, fingerprint_tuple = zip(*sorted(self.params.items(), key=lambda tup: tup[0]))
+            self._finger_print_string = str(fingerprint_tuple)
+        else:
+            self._finger_print_string = str((self.owner, self.root_out, self.seed, self.model_name, self.debug))
+        self.params.update(fingerprint=self.fingerprint)
+
+        self._csvlogger_kwargs = dict(save_dir=self.outpath, version=self.version, name=self.name)
+        self._neptune_kwargs = dict(offline_mode=self.debug,
+                                    params=self.params,
+                                    api_key=self.neptune_key,
+                                    experiment_name=self.name,
+                                    # tags=?,
+                                    project_name=self.project_name)
+        try:
+            self.neptunelogger = NeptuneLogger(**self._neptune_kwargs)
+        except ProjectNotFound as e:
+            print(f'The project "{self.project_name}" does not exist! Create it or check your spelling.')
+            print(e)
+
+        self.csvlogger = CSVLogger(**self._csvlogger_kwargs)
+        if self.params:
+            self.log_hyperparams(self.params)
+
+    def close(self):
+        self.csvlogger.close()
+        self.neptunelogger.close()
+
+    def set_fingerprint_string(self, fingerprint_str):
+        self._finger_print_string = fingerprint_str
+
+    def log_text(self, name, text, **_):
+        # TODO Implement Offline variant.
+        self.neptunelogger.log_text(name, text)
+
+    def log_hyperparams(self, params):
+        self.neptunelogger.log_hyperparams(params)
+        self.csvlogger.log_hyperparams(params)
+        pass
+
+    def log_metric(self, metric_name, metric_value, step=None, **kwargs):
+        self.csvlogger.log_metrics(dict(metric_name=metric_value, **kwargs), step=step, **kwargs)
+        self.neptunelogger.log_metric(metric_name, metric_value, step=step, **kwargs)
+        pass
+
+    def log_metrics(self, metrics, step=None):
+        self.neptunelogger.log_metrics(metrics, step=step)
+        self.csvlogger.log_metrics(metrics, step=step)
+        pass
+
+    def log_image(self, name, image, ext='png', step=None, **kwargs):
+        image_name = f'{"0" * (4 - len(str(step)))}{step}_{name}' if step is not None else name
+        image_path = self.log_dir / self.media_dir / f'{image_name}.{ext[1:] if ext.startswith(".") else ext}'
+        (self.log_dir / self.media_dir).mkdir(parents=True, exist_ok=True)
+        image.savefig(image_path, bbox_inches='tight', pad_inches=0)
+        self.neptunelogger.log_image(name, str(image_path), **kwargs)
+
+    def save(self):
+        self.csvlogger.save()
+        self.neptunelogger.save()
+
+    def finalize(self, status):
+        self.csvlogger.finalize(status)
+        self.neptunelogger.finalize(status)
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.finalize('success')
+        pass

utils/logging.py

@@ -1,116 +0,0 @@
-from abc import ABC
-from pathlib import Path
-
-from pytorch_lightning.loggers.base import LightningLoggerBase
-from pytorch_lightning.loggers.neptune import NeptuneLogger
-from pytorch_lightning.loggers.test_tube import TestTubeLogger
-
-from utils.config import Config
-
-
-class Logger(LightningLoggerBase, ABC):
-
-    media_dir = 'media'
-
-    @property
-    def experiment(self):
-        if self.debug:
-            return self.testtubelogger.experiment
-        else:
-            return self.neptunelogger.experiment
-
-    @property
-    def log_dir(self):
-        return Path(self.testtubelogger.experiment.get_logdir()).parent
-
-    @property
-    def name(self):
-        return self.config.name
-
-    @property
-    def project_name(self):
-        return f"{self.config.project.owner}/{self.config.project.name.replace('_', '-')}"
-
-    @property
-    def version(self):
-        return self.config.get('main', 'seed')
-
-    @property
-    def outpath(self):
-        return Path(self.config.train.outpath) / self.config.model.type
-
-    @property
-    def exp_path(self):
-        return Path(self.outpath) / self.name
-
-    def __init__(self, config: Config):
-        """
-        params (dict|None): Optional. Parameters of the experiment. After experiment creation params are read-only.
-           Parameters are displayed in the experiment’s Parameters section and each key-value pair can be
-           viewed in experiments view as a column.
-        properties (dict|None): Optional default is {}. Properties of the experiment.
-           They are editable after experiment is created. Properties are displayed in the experiment’s Details and
-           each key-value pair can be viewed in experiments view as a column.
-        tags (list|None): Optional default []. Must be list of str. Tags of the experiment.
-           They are editable after experiment is created (see: append_tag() and remove_tag()).
-           Tags are displayed in the experiment’s Details and can be viewed in experiments view as a column.
-        """
-        super(Logger, self).__init__()
-
-        self.config = config
-        self.debug = self.config.main.debug
-        self._testtube_kwargs = dict(save_dir=self.outpath, version=self.version, name=self.name)
-        self._neptune_kwargs = dict(offline_mode=self.debug,
-                                    api_key=self.config.project.neptune_key,
-                                    experiment_name=self.name,
-                                    project_name=self.project_name,
-                                    params=self.config.model_paramters)
-        self.neptunelogger = NeptuneLogger(**self._neptune_kwargs)
-        self.testtubelogger = TestTubeLogger(**self._testtube_kwargs)
-        self.log_config_as_ini()
-
-    def log_hyperparams(self, params):
-        self.neptunelogger.log_hyperparams(params)
-        self.testtubelogger.log_hyperparams(params)
-        pass
-
-    def log_metrics(self, metrics, step=None):
-        self.neptunelogger.log_metrics(metrics, step=step)
-        self.testtubelogger.log_metrics(metrics, step=step)
-        pass
-
-    def close(self):
-        self.testtubelogger.close()
-        self.neptunelogger.close()
-
-    def log_config_as_ini(self):
-        self.config.write(self.log_dir / 'config.ini')
-
-    def log_text(self, name, text, step_nb=0, **kwargs):
-        # TODO Implement Offline variant.
-        self.neptunelogger.log_text(name, text, step_nb)
-
-    def log_metric(self, metric_name, metric_value, **kwargs):
-        self.testtubelogger.log_metrics(dict(metric_name=metric_value))
-        self.neptunelogger.log_metric(metric_name, metric_value, **kwargs)
-
-    def log_image(self, name, image, **kwargs):
-        self.neptunelogger.log_image(name, image, **kwargs)
-        step = kwargs.get('step', None)
-        name = f'{step}_{name}' if step is not None else name
-        image.savefig(self.log_dir / self.media_dir / name)
-
-    def save(self):
-        self.testtubelogger.save()
-        self.neptunelogger.save()
-
-    def finalize(self, status):
-        self.testtubelogger.finalize(status)
-        self.neptunelogger.finalize(status)
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.finalize('success')
-        pass

utils/model_io.py

@@ -1,5 +1,7 @@
 from argparse import Namespace
 from collections import Mapping
+from typing import Union
+
 from copy import deepcopy
 from pathlib import Path
 
@@ -11,6 +13,14 @@ from torch import nn
 # Hyperparamter Object
 class ModelParameters(Namespace, Mapping):
 
+    @property
+    def as_dict(self):
+        return {key: self.get(key) if key != 'activation' else self.activation_as_string for key in self.keys()}
+
+    @property
+    def activation_as_string(self):
+        return self['activation'].lower()
+
     @property
     def module_kwargs(self):
 
@@ -18,9 +28,11 @@ class ModelParameters(Namespace, Mapping):
 
         paramter_mapping.update(
             dict(
-                activation=self._activations[self['activation']]
+                activation=self.__getattribute__('activation')
             )
         )
+        # Get rid of paramters that
+        paramter_mapping.__delitem__('in_shape')
 
         return paramter_mapping
 
@@ -42,49 +54,54 @@ class ModelParameters(Namespace, Mapping):
 
     def __getattribute__(self, name):
         if name == 'activation':
-            return self._activations[self['activation']]
+            return self._activations[self['activation'].lower()]
         else:
-            try:
-                return super(ModelParameters, self).__getattribute__(name)
-            except AttributeError as e:
-                if name == 'stretch':
-                    return False
-                else:
-                    raise AttributeError(e)
+            return super(ModelParameters, self).__getattribute__(name)
 
     _activations = dict(
         leaky_relu=nn.LeakyReLU,
+        gelu=nn.GELU,
+        elu=nn.ELU,
         relu=nn.ReLU,
         sigmoid=nn.Sigmoid,
         tanh=nn.Tanh
     )
 
     def __init__(self, parameter_mapping):
+        if isinstance(parameter_mapping, Namespace):
+            parameter_mapping = parameter_mapping.__dict__
         super(ModelParameters, self).__init__(**parameter_mapping)
 
 
 class SavedLightningModels(object):
 
     @classmethod
-    def load_checkpoint(cls, models_root_path, model=None, n=-1, tags_file_path=''):
+    def load_checkpoint(cls, models_root_path, model=None, n=-1, checkpoint: Union[None, str] = None):
         assert models_root_path.exists(), f'The path {models_root_path.absolute()} does not exist!'
-        found_checkpoints = list(Path(models_root_path).rglob('*.ckpt'))
-
-        found_checkpoints = natsorted(found_checkpoints, key=lambda y: y.name)
+        if checkpoint is not None:
+            checkpoint_path = Path(checkpoint)
+            assert checkpoint_path.exists(), f'The path ({checkpoint_path} does not exist).'
+        else:
+            found_checkpoints = list(Path(models_root_path).rglob('*.ckpt'))
+            checkpoint_path = natsorted(found_checkpoints, key=lambda y: y.name)[n]
         if model is None:
             model = torch.load(models_root_path / 'model_class.obj')
         assert model is not None
 
-        return cls(weights=found_checkpoints[n], model=model)
+        return cls(weights=checkpoint_path, model=model)
 
     def __init__(self, **kwargs):
-        self.weights: str = kwargs.get('weights', '')
+        self.weights: Path = Path(kwargs.get('weights', ''))
+        self.hparams: Path = self.weights.parent / 'hparams.yaml'
 
         self.model = kwargs.get('model', None)
         assert self.model is not None
 
     def restore(self):
-        pretrained_model = self.model.load_from_checkpoint(self.weights)
+
+        pretrained_model = self.model.load_from_checkpoint(self.weights.__str__())
+        # , hparams_file=self.hparams.__str__())
         pretrained_model.eval()
         pretrained_model.freeze()
-        return pretrained_model
+        return pretrained_model
+

utils/parallel.py

@@ -23,3 +23,5 @@ def run_n_in_parallel(f, n, processes=0, **kwargs):
         p.join()
 
     return results
+
+raise NotImplementedError()

utils/tools.py

@@ -1,6 +1,35 @@
+import importlib
+import inspect
 import pickle
 import shelve
-from pathlib import Path
+from argparse import ArgumentParser, ArgumentError
+from ast import literal_eval
+from pathlib import Path, PurePath
+from typing import Union
+
+import numpy as np
+import torch
+import random
+
+
+def auto_cast(a):
+  try:
+    return literal_eval(a)
+  except:
+    return a
+
+
+def to_one_hot(idx_array, max_classes):
+    one_hot = np.zeros((idx_array.size, max_classes))
+    one_hot[np.arange(idx_array.size), idx_array] = 1
+    return one_hot
+
+
+def fix_all_random_seeds(seed):
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    random.seed(seed)
+    print(f'Seed is now fixed: "{seed}".')
 
 
 def write_to_shelve(file_path, value):
@@ -20,4 +49,43 @@ def load_from_shelve(file_path, key):
 
 def check_path(file_path):
     assert isinstance(file_path, Path)
-    assert str(file_path).endswith('.pik')
+    assert str(file_path).endswith('.pik')
+
+
+def locate_and_import_class(class_name, folder_path: Union[str, PurePath] = ''):
+    """Locate an object by name or dotted path, importing as necessary."""
+    import sys
+    sys.path.append("..")
+    folder_path = Path(folder_path)
+    module_paths = [x for x in folder_path.rglob('*.py') if x.is_file() and '__init__' not in x.name]
+    # possible_package_path = folder_path / '__init__.py'
+    # package = str(possible_package_path) if possible_package_path.exists() else None
+    for module_path in module_paths:
+        mod = importlib.import_module('.'.join([x.replace('.py', '') for x in module_path.parts]))
+        try:
+            model_class = mod.__getattribute__(class_name)
+            return model_class
+        except AttributeError:
+           continue
+    raise AttributeError(f'Check the {folder_path.name} name. Possible files are:\n{[x.name for x in module_paths]}')
+
+
+def add_argparse_args(cls, parent_parser):
+    parser = ArgumentParser(parents=[parent_parser], add_help=False)
+    full_arg_spec = inspect.getfullargspec(cls.__init__)
+    n_non_defaults = len(full_arg_spec.args) - (len(full_arg_spec.defaults) if full_arg_spec.defaults else 0)
+    for idx, argument in enumerate(full_arg_spec.args):
+        try:
+            if argument == 'self':
+                continue
+            if idx < n_non_defaults:
+                parser.add_argument(f'--{argument}', type=int)
+            else:
+                argument_type = type(argument)
+                parser.add_argument(f'--{argument}',
+                                    type=argument_type,
+                                    default=full_arg_spec.defaults[idx - n_non_defaults]
+                                    )
+        except ArgumentError:
+            continue
+    return parser

utils/transforms.py

@@ -1,8 +1,22 @@
+from abc import ABC
 from torchvision.transforms import ToTensor as TorchVisionToTensor
 
 
+class _BaseTransformation(ABC):
+
+    def __init__(self, *args):
+        pass
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}({self.__dict__})'
+
+    def __call__(self, *args, **kwargs):
+        raise NotImplementedError
+
+
 class ToTensor(TorchVisionToTensor):
 
     def __call__(self, pic):
+        # Make it float .float() == 32bit
         tensor = super(ToTensor, self).__call__(pic).float()
         return tensor

visualization/tools.py

@@ -1,31 +1,56 @@
+try:
+    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+    from matplotlib import pyplot as plt
+except ImportError:  # pragma: no-cover
+    raise ImportError('You want to use `matplotlib` which is not installed yet,'  # pragma: no-cover
+                      ' install it with `pip install matplotlib`.')
+
 from pathlib import Path
-import matplotlib.pyplot as plt
+
+
+def prettyfy_sns():
+    plt.style.use('default')
+    try:
+        import seaborn as sns
+    except ImportError:
+        raise ImportError('You want to use `seaborn` which is not installed yet,'  # pragma: no-cover
+                          ' install it with `pip install seaborn`.')
+    sns.set_palette('Dark2')
+
+    tex_fonts = {
+        # Use LaTeX to write all text
+        "text.usetex": True,
+        "font.family": "serif",
+        # Use 10pt font in plots, to match 10pt font in document
+        "axes.labelsize": 10,
+        "font.size": 10,
+        # Make the legend/label fonts a little smaller
+        "legend.fontsize": 8,
+        "xtick.labelsize": 8,
+        "ytick.labelsize": 8
+    }
+
+    plt.rcParams.update(tex_fonts)
 
 
 class Plotter(object):
-    def __init__(self, root_path=''):
-        self.root_path = Path(root_path)
 
-    def save_current_figure(self, path, extention='.png', naked=True):
-        fig, _ = plt.gcf(), plt.gca()
+    def __init__(self, root_path=''):
+        if not root_path:
+            self.root_path = Path(root_path)
+
+    def save_figure(self, figure, title, extention='.png', naked=False):
+        canvas = FigureCanvas(figure)
         # Prepare save location and check img file extention
-        path = self.root_path / Path(path if str(path).endswith(extention) else f'{str(path)}{extention}')
+        path = self.root_path / f'{title}{extention}'
         path.parent.mkdir(exist_ok=True, parents=True)
         if naked:
-            plt.axis('off')
-            fig.savefig(path, bbox_inches='tight', transparent=True, pad_inches=0)
-            fig.clf()
+            figure.axis('off)')
+            figure.savefig(path, bbox_inches='tight', transparent=True, pad_inches=0)
+            canvas.print_figure(path)
         else:
-            fig.savefig(path)
-            fig.clf()
-
-    def show_current_figure(self):
-        fig, _ = plt.gcf(), plt.gca()
-        fig.show()
-        fig.clf()
+            canvas.print_figure(path)
 
 
 if __name__ == '__main__':
-    output_root = Path('..') / 'output'
-    p = Plotter(output_root)
-    p.save_current_figure('test.png')
+    raise PermissionError('Get out of here.')