add preliminary submission package

dcase2020_task2/experiments/__init__.py

-from dcase2020_task2.experiments.experiment_base import BaseExperiment
-from dcase2020_task2.experiments.baseline_experiment import BaselineExperiment
+from dcase2020_task2.experiments.experiment_base import BaseExperiment
\ No newline at end of file

dcase2020_task2/experiments/baseline_experiment.py

-from dcase2020_task2.experiments import BaseExperiment
-from dcase2020_task2.utils.logger import Logger
-from datetime import datetime
-import os
-import pytorch_lightning as pl
-from sacred import Experiment
-
-from sacred import SETTINGS
-SETTINGS['CAPTURE_MODE'] = 'sys'
-
-
-class BaselineExperiment(BaseExperiment, pl.LightningModule):
-
-    '''
-    DCASE Baseline with AE, MADMOG & MAF per machine ID.
-    '''
-
-    def __init__(self, configuration_dict, _run):
-        super().__init__(configuration_dict)
-
-        self.network = self.objects['model']
-        self.reconstruction = self.objects['reconstruction']
-        self.logger_ = Logger(_run, self, self.configuration_dict, self.objects)
-
-        # experiment state variables
-        self.epoch = -1
-        self.step = 0
-        self.result = None
-
-
-    def forward(self, batch):
-        batch['epoch'] = self.epoch
-        batch = self.network(batch)
-        return batch
-
-
-    def training_step(self, batch_normal, batch_num, optimizer_idx=0):
-
-        if batch_num == 0 and optimizer_idx == 0:
-            self.epoch += 1
-
-        if optimizer_idx == 0:
-            batch_normal = self(batch_normal)
-            batch_normal['loss'] = batch_normal['reconstruction_loss']
-
-            if batch_normal.get('prior_loss'):
-                batch_normal['loss'] = batch_normal['loss'] + batch_normal['prior_loss']
-
-            self.logger_.log_training_step(batch_normal, self.step)
-            self.step += 1
-        else:
-            raise AttributeError
-
-        return {
-            'loss': batch_normal['loss'],
-            'tqdm': {'loss': batch_normal['loss']},
-        }
-
-    def validation_step(self, batch, batch_num):
-        self(batch)
-        if batch_num == 0:
-            self.logger_.log_image_reconstruction(batch, self.epoch)
-        return {
-            'targets': batch['targets'],
-            'scores': batch['scores'],
-            'machine_types': batch['machine_types'],
-            'machine_ids': batch['machine_ids'],
-            'file_ids': batch['file_ids']
-        }
-
-    def validation_end(self, outputs):
-        self.logger_.log_validation(outputs, self.step, self.epoch)
-        return {}
-
-    def test_step(self, batch, batch_num):
-        return self.validation_step(batch, batch_num)
-
-    def test_end(self, outputs):
-        self.result = self.logger_.log_test(outputs)
-        self.logger_.close()
-        return self.result
-
-def configuration():
-    seed = 1220
-    deterministic = False
-    id = datetime.now().strftime("%Y-%m-%d_%H:%M:%S:%f")
-    log_path = os.path.join('experiment_logs', id)
-
-    #####################
-    # quick configuration, uses default parameters of more detailed configuration
-    #####################
-
-    machine_type = 0
-    machine_id = 0
-
-    num_mel = 128
-    n_fft = 1024
-    hop_size = 512
-    power = 2.0
-    fmin = 0
-    context = 5
-
-    model_class = 'dcase2020_task2.models.AE' # 'dcase2020_task2.models.MADE'
-    hidden_size = 512
-    num_hidden = 3
-    latent_size = 64 # only used for AEs
-
-    debug = False
-    if debug:
-        num_workers = 0
-    else:
-        num_workers = 4
-
-    epochs = 100
-    reconstruction_class = 'dcase2020_task2.losses.MSEReconstruction' # 'dcase2020_task2.losses.NLLReconstruction'
-    batch_size = 512
-    learning_rate = 1e-3
-    weight_decay = 1e-5
-
-    normalize_raw = True
-
-
-    descriptor = "BaselineExperiment_Model:[{}_{}_{}_{}]_Training:[{}_{}_{}]_Features:[{}_{}_{}_{}_{}_{}_{}]_{}".format(
-        model_class,
-        hidden_size,
-        num_hidden,
-        latent_size,
-        batch_size,
-        learning_rate,
-        weight_decay,
-        normalize_raw,
-        num_mel,
-        context,
-        n_fft,
-        hop_size,
-        power,
-        fmin,
-        seed
-    )
-
-    ########################
-    # detailed configuration
-    ########################
-
-    data_set = {
-        'class': 'dcase2020_task2.data_sets.MCMDataSet',
-        'args': [
-            machine_type,
-            machine_id
-        ],
-        'kwargs': {
-            'context': context,
-            'num_mel': num_mel,
-            'n_fft': n_fft,
-            'hop_size': hop_size,
-            'normalize_raw': normalize_raw,
-            'power': power,
-            'fmin': fmin,
-            'hop_all': False
-        }
-    }
-
-    reconstruction = {
-        'class': reconstruction_class,
-        'args': [
-            '@data_set.observation_shape',
-        ],
-        'kwargs': {
-            'weight': 1.0
-        }
-    }
-
-    model = {
-        'class': model_class,
-        'args': [
-            '@data_set.observation_shape',
-            '@reconstruction'
-        ],
-        'kwargs': {
-            'hidden_size': hidden_size,
-            'num_hidden': num_hidden,
-            'prior': {
-                'class': 'dcase2020_task2.priors.NoPrior',
-                'kwargs': {
-                    'latent_size': latent_size
-                }
-            }
-        }
-    }
-
-    lr_scheduler = {
-        'class': 'torch.optim.lr_scheduler.StepLR',
-        'args': [
-            '@optimizer',
-        ],
-        'kwargs': {
-            'step_size': 25,
-            'gamma': 0.3
-        }
-    }
-
-    optimizer = {
-        'class': 'torch.optim.Adam',
-        'args': [
-            '@model.parameters()'
-        ],
-        'kwargs': {
-            'lr': learning_rate,
-            'betas': (0.9, 0.999),
-            'amsgrad': False,
-            'weight_decay': weight_decay,
-        }
-    }
-
-    trainer = {
-        'class': 'dcase2020_task2.trainers.PTLTrainer',
-        'kwargs': {
-            'max_epochs': epochs,
-            'checkpoint_callback': False,
-            'logger': False,
-            'early_stop_callback': False,
-            'gpus': [0],
-            'show_progress_bar': True,
-            'progress_bar_refresh_rate': 1000
-        }
-    }
-
-
-ex = Experiment('dcase2020_task2_BaselineExperiment')
-cfg = ex.config(configuration)
-
-
-@ex.automain
-def run(_config, _run):
-    experiment = BaselineExperiment(_config, _run)
-    return experiment.run()

submission_package/task2/Primus_CP-JKU_task2_1/Primus_CP-JKU_task2_1.meta.yaml

@@ -9,7 +9,7 @@ submission:
 
   # Submission name
   # This name will be used in the results tables when space permits.
-  name: Vanilla Outlier Exposed ResNet
+  name: Overall Best Outlier Exposed ResNet
 
   # Submission name abbreviated
   # This abbreviated name will be used in the results table when space is tight.
@@ -102,7 +102,7 @@ system:
     # In case of ensemble approaches, add up parameters for all subsystems.
     # In case embeddings are used, add up parameter count of the embedding extraction networks and classification network.
     # Use numerical value.
-    total_parameters: 96000000
+    total_parameters: 9600000
 
   # List of external datasets used in the submission.
   # Development dataset is used here only as an example, list only external datasets
@@ -116,8 +116,7 @@ system:
 
   # URL to the source code of the system [optional, highly recommended]
   # Reproducibility will be used to evaluate submitted systems.
-  # TODO
-  source_code: !!null
+  source_code: https://gitlab.cp.jku.at/paulp/dcase2020_task2
 
 # System results
 results:

submission_package/task2/Primus_CP-JKU_task2_2/Primus_CP-JKU_task2_2.meta.yaml

@@ -9,7 +9,7 @@ submission:
 
   # Submission name
   # This name will be used in the results tables when space permits.
-  name: Outlier Exposed ResNet per Machine Type
+  name: Best Outlier Exposed ResNet per Machine Type
 
   # Submission name abbreviated
   # This abbreviated name will be used in the results table when space is tight.
@@ -102,7 +102,7 @@ system:
     # In case of ensemble approaches, add up parameters for all subsystems.
     # In case embeddings are used, add up parameter count of the embedding extraction networks and classification network.
     # Use numerical value.
-    total_parameters: 96000000
+    total_parameters: 9600000
 
   # List of external datasets used in the submission.
   # Development dataset is used here only as an example, list only external datasets
@@ -116,8 +116,7 @@ system:
 
   # URL to the source code of the system [optional, highly recommended]
   # Reproducibility will be used to evaluate submitted systems.
-  # TODO
-  source_code: !!null
+  source_code: https://gitlab.cp.jku.at/paulp/dcase2020_task2
 
 # System results
 results:

submission_package/task2/Primus_CP-JKU_task2_3/Primus_CP-JKU_task2_3.meta.yaml

@@ -9,7 +9,7 @@ submission:
 
   # Submission name
   # This name will be used in the results tables when space permits.
-  name: Outlier Exposed Convolutional Classifier Small Ensemble
+  name: Median Ensemble Outlier Exposed ResNet
 
   # Submission name abbreviated
   # This abbreviated name will be used in the results table when space is tight.
@@ -77,11 +77,11 @@ system:
     # Ensemble method subsystem count
     # In case ensemble method is not used, mark !!null.
     # e.g. 2, 3, 4, 5, ...
-    ensemble_method_subsystem_count: 2
+    ensemble_method_subsystem_count: 13
 
     # Decision making in ensemble
     # e.g. average, median, maximum, minimum, ...
-    decision_making: average
+    decision_making: median
 
     # External data usage method
     # Please specify all usages (comma-separated list).
@@ -102,7 +102,7 @@ system:
     # In case of ensemble approaches, add up parameters for all subsystems.
     # In case embeddings are used, add up parameter count of the embedding extraction networks and classification network.
     # Use numerical value.
-    total_parameters: 1248000000
+    total_parameters: 124800000
 
   # List of external datasets used in the submission.
   # Development dataset is used here only as an example, list only external datasets
@@ -116,8 +116,7 @@ system:
 
   # URL to the source code of the system [optional, highly recommended]
   # Reproducibility will be used to evaluate submitted systems.
-  # TODO
-  source_code: !!null
+  source_code: https://gitlab.cp.jku.at/paulp/dcase2020_task2
 
 # System results
 results:

submission_package/task2/Primus_CP-JKU_task2_4/Primus_CP-JKU_task2_4.meta.yaml

@@ -9,7 +9,7 @@ submission:
 
   # Submission name
   # This name will be used in the results tables when space permits.
-  name: Outlier Exposed Convolutional Classifier Large Ensemble
+  name: Mean Ensemble Outlier Exposed ResNet
 
   # Submission name abbreviated
   # This abbreviated name will be used in the results table when space is tight.
@@ -77,11 +77,11 @@ system:
     # Ensemble method subsystem count
     # In case ensemble method is not used, mark !!null.
     # e.g. 2, 3, 4, 5, ...
-    ensemble_method_subsystem_count: 4
+    ensemble_method_subsystem_count: 13
 
     # Decision making in ensemble
     # e.g. average, median, maximum, minimum, ...
-    decision_making: average
+    decision_making: mean
 
     # External data usage method
     # Please specify all usages (comma-separated list).
@@ -102,7 +102,7 @@ system:
     # In case of ensemble approaches, add up parameters for all subsystems.
     # In case embeddings are used, add up parameter count of the embedding extraction networks and classification network.
     # Use numerical value.
-    total_parameters: 1248000000
+    total_parameters: 124800000
 
   # List of external datasets used in the submission.
   # Development dataset is used here only as an example, list only external datasets
@@ -116,8 +116,7 @@ system:
 
   # URL to the source code of the system [optional, highly recommended]
   # Reproducibility will be used to evaluate submitted systems.
-  # TODO
-  source_code: !!null
+  source_code: https://gitlab.cp.jku.at/paulp/dcase2020_task2
 
 # System results
 results: