diff --git a/.gitignore b/.gitignore
index 85e7c1d..7fb11b7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,6 @@
/.idea/
+# my own stuff
+
+/data
+/.idea
+/ml_lib
\ No newline at end of file
diff --git a/examples/__init__.py b/_templates/__init__.py
similarity index 100%
rename from examples/__init__.py
rename to _templates/__init__.py
diff --git a/_templates/new_project/__init__.py b/_templates/new_project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/_templates/new_project/_parameters.py b/_templates/new_project/_parameters.py
new file mode 100644
index 0000000..751b801
--- /dev/null
+++ b/_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
\ No newline at end of file
diff --git a/_templates/new_project/datasets/template_dataset.py b/_templates/new_project/datasets/template_dataset.py
new file mode 100644
index 0000000..7f5a373
--- /dev/null
+++ b/_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__()
\ No newline at end of file
diff --git a/_templates/new_project/main.py b/_templates/new_project/main.py
new file mode 100644
index 0000000..01430a9
--- /dev/null
+++ b/_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)
diff --git a/examples/multi_run.py b/_templates/new_project/multi_run.py
similarity index 90%
rename from examples/multi_run.py
rename to _templates/new_project/multi_run.py
index 9577a4b..4b98c03 100644
--- a/examples/multi_run.py
+++ b/_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__':
diff --git a/_templates/new_project/utils/__init__.py b/_templates/new_project/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/_templates/new_project/utils/module_mixins.py b/_templates/new_project/utils/module_mixins.py
new file mode 100644
index 0000000..cc98998
--- /dev/null
+++ b/_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
diff --git a/_templates/new_project/utils/project_config.py b/_templates/new_project/utils/project_config.py
new file mode 100644
index 0000000..78774db
--- /dev/null
+++ b/_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()
diff --git a/audio_toolset/mel_augmentation.py b/audio_toolset/mel_augmentation.py
index 9a72d78..92943e3 100644
--- a/audio_toolset/mel_augmentation.py
+++ b/audio_toolset/mel_augmentation.py
@@ -1,5 +1,3 @@
-from ctypes import Union
-
import numpy as np
diff --git a/examples/main.py b/examples/main.py
deleted file mode 100644
index d3e4d8a..0000000
--- a/examples/main.py
+++ /dev/null
@@ -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)
diff --git a/examples/variables.py b/examples/variables.py
deleted file mode 100644
index 06bd575..0000000
--- a/examples/variables.py
+++ /dev/null
@@ -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
diff --git a/modules/blocks.py b/modules/blocks.py
index 04cb264..6fbd07d 100644
--- a/modules/blocks.py
+++ b/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')
diff --git a/modules/model_parts.py b/modules/model_parts.py
index 353cbf3..e0374a2 100644
--- a/modules/model_parts.py
+++ b/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):
diff --git a/modules/utils.py b/modules/utils.py
index 5ba8afb..a87b835 100644
--- a/modules/utils.py
+++ b/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:
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..5c67e65
--- /dev/null
+++ b/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
\ No newline at end of file
diff --git a/utils/config.py b/utils/config.py
index bf15954..6788803 100644
--- a/utils/config.py
+++ b/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
diff --git a/utils/logging.py b/utils/logging.py
index 645ee4f..cd3dfe6 100644
--- a/utils/logging.py
+++ b/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):
diff --git a/utils/transforms.py b/utils/transforms.py
index 945bca1..2da734c 100644
--- a/utils/transforms.py
+++ b/utils/transforms.py
@@ -1,8 +1,8 @@
-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()
- return tensor
\ No newline at end of file
+ return tensor