From fc93f716080586ed1913d882e62933019b02f4f9 Mon Sep 17 00:00:00 2001 From: Steffen Illium Date: Sun, 17 May 2020 22:05:20 +0200 Subject: [PATCH] Templates finalized --- .gitignore | 5 + {examples => _templates}/__init__.py | 0 _templates/new_project/__init__.py | 0 _templates/new_project/_parameters.py | 57 ++++++ .../new_project/datasets/template_dataset.py | 6 + _templates/new_project/main.py | 86 +++++++++ .../new_project}/multi_run.py | 4 +- _templates/new_project/utils/__init__.py | 0 _templates/new_project/utils/module_mixins.py | 172 ++++++++++++++++++ .../new_project/utils/project_config.py | 30 +++ audio_toolset/mel_augmentation.py | 2 - examples/main.py | 142 --------------- examples/variables.py | 12 -- modules/blocks.py | 2 +- modules/model_parts.py | 2 +- modules/utils.py | 2 +- requirements.txt | 92 ++++++++++ utils/config.py | 3 +- utils/logging.py | 2 +- utils/transforms.py | 6 +- 20 files changed, 459 insertions(+), 166 deletions(-) rename {examples => _templates}/__init__.py (100%) create mode 100644 _templates/new_project/__init__.py create mode 100644 _templates/new_project/_parameters.py create mode 100644 _templates/new_project/datasets/template_dataset.py create mode 100644 _templates/new_project/main.py rename {examples => _templates/new_project}/multi_run.py (90%) create mode 100644 _templates/new_project/utils/__init__.py create mode 100644 _templates/new_project/utils/module_mixins.py create mode 100644 _templates/new_project/utils/project_config.py delete mode 100644 examples/main.py delete mode 100644 examples/variables.py create mode 100644 requirements.txt 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