Templates finalized

.gitignore

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

_templates/new_project/_parameters.py

@@ -0,0 +1,57 @@
+# Imports
+# =============================================================================
+import os
+from distutils.util import strtobool
+from argparse import ArgumentParser, Namespace
+
+# Parameter Configuration
+# =============================================================================
+# Argument Parser
+main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
+
+# Main Parameters
+main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--main_eval", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
+
+# Project
+main_arg_parser.add_argument("--project_name", type=str, default='traj-gen', help="")
+main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="")
+main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.getenv('NEPTUNE_KEY'), help="")
+
+# Data Parameters
+main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
+main_arg_parser.add_argument("--data_dataset_length", type=int, default=10000, help="")
+main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
+main_arg_parser.add_argument("--data_additional_resource_root", type=str, default='res/resource/root', help="")
+main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
+
+# Transformations
+main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--transformations_normalize", type=strtobool, default=False, help="")
+
+# Transformations
+main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
+main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
+main_arg_parser.add_argument("--train_epochs", type=int, default=500, help="")
+main_arg_parser.add_argument("--train_batch_size", type=int, default=200, help="")
+main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
+main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
+
+# Model
+main_arg_parser.add_argument("--model_type", type=str, default="CNNRouteGenerator", help="")
+
+main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
+main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
+main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=False, help="")
+main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
+
+# Model 2: Layer Specific Stuff
+main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
+main_arg_parser.add_argument("--model_features", type=int, default=16, help="")
+
+# Parse it
+args: Namespace = main_arg_parser.parse_args()
+
+if __name__ == '__main__':
+    pass

_templates/new_project/datasets/template_dataset.py

@@ -0,0 +1,6 @@
+from torch.utils.data import Dataset
+
+
+class TemplateDataset(Dataset):
+    def __init__(self, *args, **kwargs):
+        super(TemplateDataset, self).__init__()

_templates/new_project/main.py

@@ -0,0 +1,86 @@
+# Imports
+# =============================================================================
+
+import warnings
+
+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
+
+warnings.filterwarnings('ignore', category=FutureWarning)
+warnings.filterwarnings('ignore', category=UserWarning)
+
+
+def run_lightning_loop(config_obj):
+
+    # Logging
+    # ================================================================================
+    # Logger
+    with Logger(config_obj) as logger:
+        # Callbacks
+        # =============================================================================
+        # Checkpoint Saving
+        checkpoint_callback = ModelCheckpoint(
+            filepath=str(logger.log_dir / 'ckpt_weights'),
+            verbose=True, save_top_k=0,
+        )
+
+        # =============================================================================
+        # Early Stopping
+        # TODO: For This to work, one must set a validation step and End Eval and Score
+        early_stopping_callback = EarlyStopping(
+            monitor='val_loss',
+            min_delta=0.0,
+            patience=0,
+        )
+
+        # Model
+        # =============================================================================
+        # 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
+        # =============================================================================
+        trainer = Trainer(max_epochs=config_obj.train.epochs,
+                          show_progress_bar=True,
+                          weights_save_path=logger.log_dir,
+                          gpus=[0] if torch.cuda.is_available() else None,
+                          check_val_every_n_epoch=10,
+                          # num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
+                          # row_log_interval=(model.n_train_batches * 0.1),  # TODO: Better Value / Setting
+                          # log_save_interval=(model.n_train_batches * 0.2),  # TODO: Better Value / Setting
+                          checkpoint_callback=checkpoint_callback,
+                          logger=logger,
+                          fast_dev_run=config_obj.main.debug,
+                          early_stop_callback=None
+                          )
+
+        # Train It
+        trainer.fit(model)
+
+        # Save the last state & all parameters
+        trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
+        model.save_to_disk(logger.log_dir)
+
+        # Evaluate It
+        if config_obj.main.eval:
+            trainer.test()
+
+    return model
+
+
+if __name__ == "__main__":
+    from _templates.new_project._parameters import args
+    config = Config.read_namespace(args)
+    trained_model = run_lightning_loop(config)

_templates/new_project/multi_run.py

@@ -1,6 +1,6 @@
 import warnings
 
-from ml_lib.utils.config import Config
+from utils.config import Config
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)
@@ -8,7 +8,7 @@ warnings.filterwarnings('ignore', category=UserWarning)
 # Imports
 # =============================================================================
 
-from main import run_lightning_loop, args
+from _templates.new_project.main import run_lightning_loop, args
 
 
 if __name__ == '__main__':

_templates/new_project/utils/module_mixins.py

@@ -0,0 +1,172 @@
+from collections import defaultdict
+
+from abc import ABC
+from argparse import Namespace
+
+import torch
+
+from torch import nn
+from torch.optim import Adam
+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 audio_toolset.audio_io import NormalizeLocal
+from modules.utils import LightningBaseModule
+from utils.transforms import ToTensor
+
+from _templates.new_project.utils.project_config import GlobalVar as GlobalVars
+
+
+class BaseOptimizerMixin:
+
+    def configure_optimizers(self):
+        assert isinstance(self, LightningBaseModule)
+        opt = Adam(params=self.parameters(), lr=self.params.lr, weight_decay=self.params.weight_decay)
+        if self.params.sto_weight_avg:
+            # TODO: Make this glabaly available.
+            opt = SWA(opt, swa_start=10, swa_freq=5, swa_lr=0.05)
+        return opt
+
+    def on_train_end(self):
+        assert isinstance(self, LightningBaseModule)
+        for opt in self.trainer.optimizers:
+            if isinstance(opt, SWA):
+                opt.swap_swa_sgd()
+
+    def on_epoch_end(self):
+        assert isinstance(self, LightningBaseModule)
+        if self.params.opt_reset_interval:
+            if self.current_epoch % self.params.opt_reset_interval == 0:
+                for opt in self.trainer.optimizers:
+                    opt.state = defaultdict(dict)
+
+
+class BaseTrainMixin:
+
+    absolute_loss = nn.L1Loss()
+    nll_loss = nn.NLLLoss()
+    bce_loss = nn.BCELoss()
+
+    def training_step(self, batch_xy, batch_nb, *_, **__):
+        assert isinstance(self, LightningBaseModule)
+        batch_x, batch_y = batch_xy
+        y = self(batch_x).main_out
+        bce_loss = self.bce_loss(y, batch_y)
+        return dict(loss=bce_loss, log=dict(batch_nb=batch_nb))
+
+    def training_epoch_end(self, outputs):
+        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
+
+
+class BaseValMixin:
+
+    absolute_loss = nn.L1Loss()
+    nll_loss = nn.NLLLoss()
+    bce_loss = nn.BCELoss()
+
+    def validation_step(self, batch_xy, batch_idx, _, *__, **___):
+        assert isinstance(self, LightningBaseModule)
+        batch_x, batch_y = batch_xy
+        y = self(batch_x).main_out
+        val_bce_loss = self.bce_loss(y, batch_y)
+        return dict(val_bce_loss=val_bce_loss,
+                    batch_idx=batch_idx, y=y, batch_y=batch_y)
+
+    def validation_epoch_end(self, outputs, *_, **__):
+        assert isinstance(self, LightningBaseModule)
+        summary_dict = dict(log=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}
+                                   )
+        """
+        # Additional Score like the unweighted Average Recall:
+        # UnweightedAverageRecall
+        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()
+
+        y_pred = (y_pred >= 0.5).astype(np.float32)
+
+        uar_score = sklearn.metrics.recall_score(y_true, y_pred, labels=[0, 1], average='macro',
+                                                 sample_weight=None, zero_division='warn')
+
+        summary_dict['log'].update({f'uar_score': uar_score})
+        """
+
+        return summary_dict
+
+
+class BinaryMaskDatasetMixin:
+
+    def build_dataset(self):
+        assert isinstance(self, LightningBaseModule)
+
+        # Dataset
+        # =============================================================================
+        # Data Augmentations or Utility Transformations
+
+        transforms = Compose([NormalizeLocal(), ToTensor()])
+
+        # Dataset
+        dataset = Namespace(
+            **dict(
+                # TRAIN DATASET
+                train_dataset=TemplateDataset(self.params.root, setting=GlobalVars.DATA_OPTIONS.train,
+                                              transforms=transforms
+                                              ),
+
+                # VALIDATION DATASET
+                val_dataset=TemplateDataset(self.params.root, setting=GlobalVars.vali,
+                                            ),
+
+                # TEST DATASET
+                test_dataset=TemplateDataset(self.params.root, setting=GlobalVars.test,
+                                             ),
+
+            )
+        )
+        return dataset
+
+
+class BaseDataloadersMixin(ABC):
+
+    # Dataloaders
+    # ================================================================================
+    # Train Dataloader
+    def train_dataloader(self):
+        assert isinstance(self, LightningBaseModule)
+        # In case you want to implement bootstraping
+        # sampler = RandomSampler(self.dataset.train_dataset, True, len(self.dataset.train_dataset))
+        sampler = None
+        return DataLoader(dataset=self.dataset.train_dataset, shuffle=True if not sampler else None, sampler=sampler,
+                          batch_size=self.params.batch_size,
+                          num_workers=self.params.worker)
+
+    # Test Dataloader
+    def test_dataloader(self):
+        assert isinstance(self, LightningBaseModule)
+        return DataLoader(dataset=self.dataset.test_dataset, shuffle=False,
+                          batch_size=self.params.batch_size,
+                          num_workers=self.params.worker)
+
+    # Validation Dataloader
+    def val_dataloader(self):
+        assert isinstance(self, LightningBaseModule)
+        val_dataloader = DataLoader(dataset=self.dataset.val_dataset, shuffle=False,
+                                    batch_size=self.params.batch_size, num_workers=self.params.worker)
+        # Alternative return [val_dataloader, alternative dataloader], there will be a dataloader_idx in validation_step
+        return val_dataloader

_templates/new_project/utils/project_config.py

@@ -0,0 +1,30 @@
+from argparse import Namespace
+
+from utils.config import Config
+
+
+class GlobalVar(Namespace):
+    # Labels for classes
+    LEFT = 1
+    RIGHT = 0
+    WRONG = -1
+
+    # Colors for img files
+    WHITE = 255
+    BLACK = 0
+
+    # Variables for plotting
+    PADDING = 0.25
+    DPI = 50
+
+    # DATAOPTIONS
+    train='train',
+    vali='vali',
+    test='test'
+
+
+class ThisConfig(Config):
+
+    @property
+    def _model_map(self):
+        return dict()

audio_toolset/mel_augmentation.py

@@ -1,5 +1,3 @@
-from ctypes import Union
-
 import numpy as np
 
 

examples/main.py

@@ -1,142 +0,0 @@
-# Imports
-# =============================================================================
-import os
-from distutils.util import strtobool
-from pathlib import Path
-from argparse import ArgumentParser, Namespace
-
-import warnings
-
-import torch
-from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
-
-from ml_lib.modules.utils import LightningBaseModule
-from ml_lib.utils.config import Config
-from ml_lib.utils.logging import Logger
-from ml_lib.utils.model_io import SavedLightningModels
-
-warnings.filterwarnings('ignore', category=FutureWarning)
-warnings.filterwarnings('ignore', category=UserWarning)
-
-_ROOT = Path(__file__).parent
-
-# Parameter Configuration
-# =============================================================================
-# Argument Parser
-main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
-
-# Main Parameters
-main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--main_eval", type=strtobool, default=True, help="")
-main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
-
-# Data Parameters
-main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
-main_arg_parser.add_argument("--data_dataset_length", type=int, default=10000, help="")
-main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
-main_arg_parser.add_argument("--data_map_root", type=str, default='res/shapes', help="")
-main_arg_parser.add_argument("--data_normalized", type=strtobool, default=True, help="")
-main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
-
-main_arg_parser.add_argument("--data_mode", type=str, default='vae_no_label_in_map', help="")
-
-# Transformations
-main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
-
-# Transformations
-main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
-main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
-main_arg_parser.add_argument("--train_epochs", type=int, default=500, help="")
-main_arg_parser.add_argument("--train_batch_size", type=int, default=200, help="")
-main_arg_parser.add_argument("--train_lr", type=float, default=1e-3, help="")
-main_arg_parser.add_argument("--train_num_sanity_val_steps", type=int, default=0, help="")
-
-# Model
-main_arg_parser.add_argument("--model_type", type=str, default="CNNRouteGenerator", help="")
-main_arg_parser.add_argument("--model_activation", type=str, default="leaky_relu", help="")
-main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
-main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
-main_arg_parser.add_argument("--model_lat_dim", type=int, default=16, help="")
-main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
-main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--model_use_res_net", type=strtobool, default=False, help="")
-main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
-
-# Project
-main_arg_parser.add_argument("--project_name", type=str, default='traj-gen', help="")
-main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="")
-main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.getenv('NEPTUNE_KEY'), help="")
-
-# Parse it
-args: Namespace = main_arg_parser.parse_args()
-
-
-def run_lightning_loop(config_obj):
-
-    # Logging
-    # ================================================================================
-    # Logger
-    with Logger(config_obj) as logger:
-        # Callbacks
-        # =============================================================================
-        # Checkpoint Saving
-        checkpoint_callback = ModelCheckpoint(
-            filepath=str(logger.log_dir / 'ckpt_weights'),
-            verbose=True, save_top_k=0,
-        )
-
-        # =============================================================================
-        # Early Stopping
-        # TODO: For This to work, one must set a validation step and End Eval and Score
-        early_stopping_callback = EarlyStopping(
-            monitor='val_loss',
-            min_delta=0.0,
-            patience=0,
-        )
-
-        # Model
-        # =============================================================================
-        # 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
-        # =============================================================================
-        trainer = Trainer(max_epochs=config_obj.train.epochs,
-                          show_progress_bar=True,
-                          weights_save_path=logger.log_dir,
-                          gpus=[0] if torch.cuda.is_available() else None,
-                          check_val_every_n_epoch=10,
-                          # num_sanity_val_steps=config_obj.train.num_sanity_val_steps,
-                          # row_log_interval=(model.n_train_batches * 0.1),  # TODO: Better Value / Setting
-                          # log_save_interval=(model.n_train_batches * 0.2),  # TODO: Better Value / Setting
-                          checkpoint_callback=checkpoint_callback,
-                          logger=logger,
-                          fast_dev_run=config_obj.main.debug,
-                          early_stop_callback=None
-                          )
-
-        # Train It
-        trainer.fit(model)
-
-        # Save the last state & all parameters
-        trainer.save_checkpoint(logger.log_dir / 'weights.ckpt')
-        model.save_to_disk(logger.log_dir)
-
-        # Evaluate It
-        if config_obj.main.eval:
-            trainer.test()
-
-    return model
-
-
-if __name__ == "__main__":
-
-    config = Config.read_namespace(args)
-    trained_model = run_lightning_loop(config)

examples/variables.py

@@ -1,12 +0,0 @@
-# Labels for classes
-HOMOTOPIC = 1
-ALTERNATIVE = 0
-ANY = -1
-
-# Colors for img files
-WHITE = 255
-BLACK = 0
-
-# Variables for plotting
-PADDING = 0.25
-DPI = 50

modules/blocks.py

@@ -5,7 +5,7 @@ import warnings
 
 from torch import nn
 
-from ml_lib.modules.utils import AutoPad, Interpolate, ShapeMixin, F_x, Flatten
+from modules.utils import AutoPad, Interpolate, ShapeMixin, F_x, Flatten
 
 DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 

modules/model_parts.py

@@ -4,7 +4,7 @@
 import torch
 from torch import nn
 
-from ml_lib.modules.utils import ShapeMixin
+from modules.utils import ShapeMixin
 
 
 class Generator(nn.Module):

modules/utils.py

@@ -10,7 +10,7 @@ import pytorch_lightning as pl
 
 # Utility - Modules
 ###################
-from ml_lib.utils.model_io import ModelParameters
+from utils.model_io import ModelParameters
 
 
 class ShapeMixin:

requirements.txt

@@ -0,0 +1,92 @@
+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
+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

utils/config.py

@@ -70,7 +70,8 @@ class Config(ConfigParser, ABC):
         try:
             return self._model_map[self.model.type]
         except KeyError:
-            raise KeyError(rf'The model alias you provided ("{self.get("model", "type")}") does not exist! Try one of these: {list(self._model_map.keys())}')
+            raise KeyError(f'The model alias you provided ("{self.get("model", "type")}")' +
+                           'does not exist! Try one of these: {list(self._model_map.keys())}')
 
     # TODO: Do this programmatically; This did not work:
     # Initialize Default Sections as Property

utils/logging.py

@@ -5,7 +5,7 @@ from pytorch_lightning.loggers.base import LightningLoggerBase
 from pytorch_lightning.loggers.neptune import NeptuneLogger
 from pytorch_lightning.loggers.test_tube import TestTubeLogger
 
-from ml_lib.utils.config import Config
+from utils.config import Config
 
 
 class Logger(LightningLoggerBase, ABC):

utils/transforms.py

@@ -1,7 +1,7 @@
-from torchvision.transforms import ToTensor as TorchvisionToTensor
+from torchvision.transforms import ToTensor as TorchVisionToTensor
 
 
-class ToTensor(TorchvisionToTensor):
+class ToTensor(TorchVisionToTensor):
 
     def __call__(self, pic):
         tensor = super(ToTensor, self).__call__(pic).float()