j030

mergernet/jobs/j030.py

@@ -0,0 +1,76 @@
+from itertools import compress
+
+import pandas as pd
+
+from mergernet.core.constants import DATA_ROOT
+from mergernet.core.experiment import Experiment, backup_model
+from mergernet.core.hp import HP, HyperParameterSet
+from mergernet.core.utils import iauname, iauname_path
+from mergernet.data.dataset import Dataset
+from mergernet.data.image import (ChannelAverage, Crop, ImagePipeline,
+                                  LegacyRGB, TensorToImage, TensorToShards)
+from mergernet.estimators.automl import OptunaEstimator
+from mergernet.estimators.parametric import ParametricEstimator
+from mergernet.services.legacy import LegacyService
+
+
+class Job(Experiment):
+  """Base model"""
+  def __init__(self):
+    super().__init__()
+    self.exp_id = 30
+    self.log_wandb = True
+    self.restart = False
+
+
+  def call(self):
+    hps = HyperParameterSet(
+      HP.const('architecture', 'efficientnetv2b0'),
+      HP.const('pretrained_weights', 'imagenet'),
+      HP.const('metrics', ['f1', 'recall', 'roc']),
+      HP.const('positive_class_id', 1),
+      HP.const('negative_class_id', 0),
+      HP.const('epochs', 50),
+      HP.const('tl_epochs', 12),
+      HP.const('t1_opt', 'adamw'),
+      HP.num('t1_lr', low=2e-4, high=5e-3, log=True),
+      HP.const('optimizer', 'adamw'),
+      HP.const('lr_decay', 'cosine_restarts'),
+      HP.const('lr_decay_steps', 4),
+      HP.const('lr_decay_t', 1.5),
+      HP.const('lr_decay_m', 1.0),
+      HP.const('lr_decay_alpha', 0.1),
+      HP.num('opt_lr', low=1e-5, high=1e-3, log=True),
+      HP.num('weight_decay', low=1e-4, high=1e-1),
+      HP.num('label_smoothing', low=0, high=0.1),
+      HP.num('batch_size', low=64, high=256, step=64, dtype=int),
+      HP.const('features_reduction', 'avg_pooling'),
+      HP.num('dense_1_units', low=32, high=1024, step=1, dtype=int),
+      HP.cat('dropout_1_rate', [None, 0.1, 0.2, 0.3, 0.4]),
+      HP.cat('batch_norm_1', [True, False]),
+      HP.num('dense_2_units', low=32, high=1024, step=1, dtype=int),
+      HP.cat('dropout_2_rate', [None, 0.1, 0.2, 0.3, 0.4]),
+      HP.cat('batch_norm_2', [True, False]),
+      HP.num('dense_3_units', low=32, high=1024, step=1, dtype=int),
+      HP.cat('dropout_3_rate', [None, 0.1, 0.2, 0.3, 0.4]),
+      HP.cat('batch_norm_3', [True, False]),
+    )
+    ds = Dataset(config=Dataset.registry.LS10_TRAIN_224_PNG)
+
+    model = ParametricEstimator(hp=hps, dataset=ds)
+
+    optuna_model = OptunaEstimator(
+      estimator=model,
+      n_trials=20,
+      objective_metric='val_recall',
+      objective_direction='maximize',
+      resume=True,
+    )
+
+    optuna_model.train()
+
+    Experiment.upload_file_gd('model.h5', optuna_model.tf_model)
+
+
+if __name__ == '__main__':
+  Job().run()